Journal articles: 'Semantics Psycholinguistics Human information processing' – Grafiati (2024)

Abstract:

Early language acquisition is a complex cognitive task. Recent data-informed approaches showed that children do not learn words uniformly at random but rather follow specific strategies based on the associative representation of words in the mental lexicon, a conceptual system enabling human cognitive computing. Building on this evidence, the current investigation introduces a combination of machine learning techniques, psycholinguistic features (i.e., frequency, length, polysemy and class) and multiplex lexical networks, representing the semantics and phonology of the mental lexicon, with the aim of predicting normative acquisition of 529 English words by toddlers between 22 and 26 months. Classifications using logistic regression and based on four psycholinguistic features achieve the best baseline cross-validated accuracy of 61.7% when half of the words have been acquired. Adding network information through multiplex closeness centrality enhances accuracy (up to 67.7%) more than adding multiplex neighbourhood density/degree (62.4%) or multiplex PageRank versatility (63.0%) or the best single-layer network metric, i.e., free association degree (65.2%), instead. Multiplex closeness operationalises the structural relevance of words for semantic and phonological information flow. These results indicate that the whole, global, multi-level flow of information and structure of the mental lexicon influence word acquisition more than single-layer or local network features of words when considered in conjunction with language norms. The highlighted synergy of multiplex lexical structure and psycholinguistic norms opens new ways for understanding human cognition and language processing through powerful and data-parsimonious cognitive computing approaches.

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Language makes human communication possible. Apart from everyday applications, language can provide insights into individuals’ thinking and reasoning. Machine-based analyses of text are becoming widespread in business applications, but their utility in learning contexts are a neglected area of research. Therefore, the goal of the present work is to explore machine-assisted approaches to aid in the analysis of students’ written compositions. A method for extracting common topics from written text is applied to 78 student papers on technology and ethics. The primary tool for analysis is the Latent Dirichlet Allocation algorithm. The results suggest that this machine-based topic extraction method is effective and supports a promising prospect for enhancing classroom learning and instruction. The method may also prove beneficial in other applied applications, like those in clinical and counseling practice. References Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022. Bruner, J. (1990). Acts of meaning. Cambridge, MA: Harvard University Press. Chen, K. Y. M., & Wang, Y. (2007). Latent dirichlet allocation. http://acsweb.ucsd.edu/~yuw176/ report/lda.pdf. Chung, C. K., & Pennebaker, J. W. (2008). Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language. Journal of research in personality, 42(1), 96-132. Feldman, S. (1999). NLP meets the Jabberwocky: Natural language processing in information retrieval. Online Magazine, 23, 62-73. Retrieved from: http://www.onlinemag.net/OL1999/ feldmann5.html Mishlove, J. (2010). https://www.youtube.com/watch?v=0XTDLq34M18 (Accessed June 12, 2018). Ostrowski, D. A. (2015). Using latent dirichlet allocation for topic modelling in twitter. In Semantic Computing (ICSC), 2015 IEEE International Conference (pp. 493-497). IEEE. Pennebaker, J. W. (2004). Theories, therapies, and taxpayers: On the complexities of the expressive writing paradigm. Clinical Psychology: Science and Practice, 11(2), 138-142. Pennebaker, J.W., Boyd, R.L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC 2015. Austin, TX: University of Texas at Austin. Pennebaker, J. W., Chung, C. K., Frazee, J., Lavergne, G. M., & Beaver, D. I. (2014). When small words foretell academic success: The case of college admissions essays. PLoS ONE, 9(12), e115844. Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296-1312. Recchia, G., Sahlgren, M., Kanerva, P., & Jones, M. N. (2015). Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Computational intelligence and neuroscience, 2015, 1-18. Salzmann, Z. (2004). Language, Culture, and Society: An Introduction to Linguistic Anthropology (3rd ed). Westview Press. Schank, R. C., Goldman, N. M., Rieger III, C. J., & Riesbeck, C. (1973). MARGIE: Memory analysis response generation, and inference on English. In IJCAI, 3, 255-261. Taraban, R., Marcy, W. M., LaCour Jr., M. S., & Burgess II, R. A. (2017). Developing machine-assisted analysis of engineering students’ ethics course assignments. Proceedings of the American Society of Engineering Education (ASEE) Annual Conference, Columbus, OH. https://www.asee.org/public/conferences/78/papers/19234/view. Taraban, R., Marcy, W. M., LaCour, M. S., Pashley, D., & Keim, K. (2018). Do engineering students learn ethics from an ethics course? Proceedings of the American Society of Engineering Education – Gulf Southwest (ASEE-GSW) Annual Conference, Austin, TX. http://www.aseegsw18.com/papers.html. Taraban, R., & Marshall, P. H. (2017). Deep learning and competition in psycholinguistic research. East European Journal of Psycholinguistics, 4(2), 67-74. Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. Winograd, T. (1972). Understanding natural language. New York: Academic Press.

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AbstractIn this article we give a short introduction to the online method of event-related (brain) potentials (ERPs) and their importance for our understanding of language structure and grammar. This methodology places high demands on (technical) requirements for laboratory equipment as well as on the skills of the investigator. However, the high costs are relatively balanced compared to the advantages of this experimental method. By using ERPs, it becomes possible to monitor the electrophysiological brain activity associated with speech processing in real time (millisecond by millisecond) and to draw conclusions on human language processing and the human parser.First, we present briefly how this method works and how ERPs can be classified (Section 1 and 2). In the following, we show that the ERP method can be used to study the processing of e. g. semantic, pragmatic and syntactic information (Section 3). Crucial for our discussion will be the interpretation of the so-called ERP components and their connection and importance for psycholinguistics and theoretical linguistics. In our presentation, we emphasize, that the electrophysiological brain activity in relation to specific (e. g. linguistic) stimuli can be used to identify distinct processes, which give a deeper insight into the different processing steps of language. At the end of this article (Section 4), we present some results from ERP studies of German negative-polar elements. Additionally, we highlight the advantage and benefits of an alternative method to analyze ERP data compared to the more ‘classical’ average technique.

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The article considers neurophysiological and psycholinguistic motivations for applying mismatch negativity (MMN) to studying the language function, briefly reviews the current evidence in the field, and offers some further directions for research in this area. MMN, a well-known index of automatic acoustic change detection, has also been found to be a sensitive indicator of long-term memory traces for native language sounds (phonemes, syllables). When comparing MMNs to words and meaningless pseudowords, we found larger amplitudes for words than for meaningless items. This was interpreted as a neurophysiological signature of word-specific memory circuits/cell assemblies activated in the human brain in a largely automatic and attention-independent fashion. This lexical enhancement of the word-elicited MMN has now been replicated by different groups using different languages and methodologies. We have also demonstrated that, using MMN, it is possible to register differences in the brain response to individual words and even to different aspects of referential semantics, confirming that the cortical memory circuits of individual lexical items can be revealed by the MMN. In other studies, we found evidence that the mismatch negativity reflects automatic syntactic processing commencing as early as ~100 ms after relevant information becomes available in the acoustic input. More recently, MMN responses were found to be sensitive to semantic context integration processes. In summary, neurophysiological imaging of the MMN response provides a unique opportunity to see subtle spatio-temporal dynamics of the neural processes underlying the language function in the human cortex in lexical, semantic, and syntactic domains.

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While the virality of misinformation has been recognized as one of the significant global issues in the modern societies, few studies had examined the computational approaches to represent and identify false information in health domains. The current study aimed at using both psycholinguistic and natural language processing models to represent verified true and false texts about human papillomavirus (HPV) vaccines. Compared to the conventional word-embedding models representing texts in the levels of words, sentences or documents, results showed that introducing the embedding in the levels of propositions best differentiated the semantic representations in true and false texts. The study would advance our understandings in representing health texts and have implications on detecting false health information.

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The author updates N.I. Zhinkin’s idea that the language, intelligence, sensing are the mechanizms for getting and processing of information. Any objective perception is the essentially result of polymodal activity, so «significatums of a natural language demand the corpus and emotions being semantically functional». The program of generalization of all extra and inner impacts play the great role in the formation of language capacity. The idea of the unity of somatic and psychological processes became the basis of the new tendency of psycholinguistics – so called corporeal semantics.

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Psycholinguistics or psychology of language is the study of the interrelation between linguistic factors and psychological aspects. Psycholinguistics studies the psychological and neurological factors that enable human to acquire, use and understand language. Psycholinguistics mainly concern with the use of psychological / scientific / experimental methods to study language acquisition, production and processing. In this study is to reveal theoretical information about psycholinguistics.

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Linguistic and psycholinguistic evidence is presented to support the use of structure-mapping theory as a framework for understanding effects of iconicity on sign language grammar and processing. The existence of structured mappings between phonological form and semantic mental representations has been shown to explain the nature of metaphor and pronominal anaphora in sign languages. With respect to processing, it is argued that psycholinguistic effects of iconicity may only be observed when the task specifically taps into such structured mappings. In addition, language acquisition effects may only be observed when the relevant cognitive abilities are in place (e.g. the ability to make structural comparisons) and when the relevant conceptual knowledge has been acquired (i.e. information key to processing the iconic mapping). Finally, it is suggested that iconicity is better understood as a structured mapping between two mental representations than as a link between linguistic form and human experience.

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In psycholinguistic research, there has been considerable interest in understanding the interactions of difFerent types of linguistic information during language processing. For example, does syntactic information interact with semantic or pragmatic information at an early stage of language processing, or only at later stages in order to resolve ambiguities of language? Developing reliable measures of language processes such as syntax and semantics is important to address many of these theoretical issues in psycholinguistics. In the present study, event-related brain potentials (ERPs) were recorded from healthy young subjects while they read pairs of words presented one word at a time. The ERPs for the second word of each pair were compared as a function of whether the preceding word was or was not (1) semantically related (i.e., synonyms; “semantic condition”) or (2) grammatically correct (“syntactic condition”). In the semantic condition the ERPs obtained to words preceded by nonsemantically related words elicited an N400 component that was maximal over centroparietal scalp regions. In contrast, in the syntactic condition the ERPs obtained to words preceded by grammatically incorrect articles or pronouns yielded a negativity with a later onset, and a frontopolar, left hemisphere scalp maximum. This replicates our previous findings of a syntactic negativity in a word pair design that was performed in the German language. Further, the present data provide scalp distributional information, which suggests that the syntactic negativity represents brain processes that are dissociable from the centroparietal N400 component. Thus, these findings provide strong evidence for a separate negative polarity ERP component that indexes syntactic aspects of language processing.

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Human languages can express opposite propositions by means of the negative operator “not,” which turns affirmative sentences into negative ones. Psycholinguistic research has indicated that negative meanings are formed by transiently reducing the access to mental representations of negated conceptual information. Neuroimaging studies have corroborated these findings, showing reduced activation of concept-specific embodied neural systems by negative versus affirmative sentences. This “disembodiment effect” of sentential negation should have two distinct consequences: first, the embodied systems should be computationally more free to support concurrent tasks when processing negative than affirmative sentences; second, the computational interference should only be reduced when there is a strict semantic congruency between the negated concept and the referent targeted by concurrent tasks. We tested these two predictions in two complementary experiments involving the comprehension of action-related sentences and kinematic measurements of its effects on concurrent, congruent actions. Sentences referred to actions involving either proximal or distal arm musculature. In experiment 1, requiring a proximal arm movement, we found interference reduction for negative proximal sentences. In experiment 2, requiring a distal arm movement, we found interference reduction for negative distal sentences. This dissociation provides the first conclusive evidence in support of a disembodiment theory of negation. We conclude that the computational cost resulting from the insertion of an additional lexical item (“not”) in negative sentences is compensated by solely storing a concept in affirmative form in semantic memory, since its negative counterpart can be produced by transiently reducing the access to such stored semantic information.

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Phrasal semantics is concerned with how the meaning of a sentence is composed both from the meaning of the constituent words, and from extra meaning contained within the structural organization of the sentence itself. In this context, grammatical constructions correspond to form-meaning mappings that essentially capture this “extra” meaning and allow its representation. The current research examines how a computational model of language processing based on a construction grammar approach can account for aspects of descriptive, referential and information content of phrasal semantics.

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Document Image Processing (DIP) provides an efficient computer storage of information. Efficient retrieval of information is equally important and requires good indexing. This is a semantics prob lem only human indexers can solve. Data banks even more than books require good indexers to provide efficient access to information. ‘SIGNPOSTS’ provides a matrix for establishing a fully articulated index for databases.

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The article deals with the problem of integration of scientific and technological progress, in particular information and communication technologies in human life, the emergence of an alternative level of dependence of the individual on information and technogenic factors in the psycholinguistic aspect. Analysis of theoretical and practical prerequisites of influence of scientific and technological sphere on a person, particularly information and communication technologies on its psycholinguistic potential, estimation of depth, and practical consequences of influence and further prospects. The general scientific methods of analysis and synthesis of the research problem were used, formalizing the results of domestic and foreign sources, systematization, and comparing existing estimates to identify significant and minor factors and their correlation, generalization of results, forecasting of prospects. Scientific and technological progress in changing man's world has adjusted his ability to choose information, analyze it critically, and formulate objective decisions. More and more people are given not information for analysis but conclusions. The existence of human dependence on information technology comes from the understanding that their creation is a projection of the inner world of man, his views and motives. Therefore, dependence is a downside to existing technological comfort, living conditions, and communication. At the same time, competition requires constant updating of people's knowledge of technology, which only exacerbates this dependency, personal energy costs, psychological fatigue, and problems of motivation. One of the conditions for effective human existence in the information and communication system is violated - synchronization of information processing processes, which negatively affects the psycholinguistic level. The level of human dependence on technology is unprecedented, and further expansion and self-interest will only worsen, in particular psycholinguistic perspectives. The improvement of the situation does not lie in the technological plane, as it increases the level of social morality, an ethos of man and community. Keywords: scientific and technological sphere, information and communication technologies, psycholinguistics, integration, influence.

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Semantic representations capture the statistics of experience and store this information in memory. A fundamental component of this memory system is knowledge of the visual environment, including knowledge of objects and their associations. Visual semantic information underlies a range of behaviors, from perceptual categorization to cognitive processes such as language and reasoning. Here we examine the neuroanatomic system that encodes visual semantics. Across three experiments, we found converging evidence indicating that knowledge of verbally mediated visual concepts relies on information encoded in a region of the ventral-medial temporal lobe centered on parahippocampal cortex. In an fMRI study, this region was strongly engaged by the processing of concepts relying on visual knowledge but not by concepts relying on other sensory modalities. In a study of patients with the semantic variant of primary progressive aphasia (semantic dementia), atrophy that encompassed this region was associated with a specific impairment in verbally mediated visual semantic knowledge. Finally, in a structural study of healthy adults from the fMRI experiment, gray matter density in this region related to individual variability in the processing of visual concepts. The anatomic location of these findings aligns with recent work linking the ventral-medial temporal lobe with high-level visual representation, contextual associations, and reasoning through imagination. Together, this work suggests a critical role for parahippocampal cortex in linking the visual environment with knowledge systems in the human brain.

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In this article we would like to show the need for developing knowledge access systems that can account for the imperfections in human perception, information processing and memory (Higgins et al., 1996). The implementation of such systems will result in enormous savings in the process of learning at all three stages of knowledge acquisition (by the mind): (1) access knowledge to, (2) learning and (3) knowledge retention (Clark et al., 1997). In particular, we will try to stress the importance of repetition spacing algorithms (Woźniak and Gorzelańczyk, 1994), as well as the importance of (1) (2) and the application of the newly introduced concept of processing, ordinal attributes in hypertext documents, semantics (Wiesman et al., 1997; Gillham, 1988).

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Human-centric computing has grown to be the major influence in today's computing research. Due to demand from industry and even lawmakers for easy-to-use computer systems, the user is now regarded as being the center of a ubiquitously available environment that supports the execution of task and anticipates user actions. This environment allows for the establishment of completely new ways for the delivery of legacy services and represents an opportunity for the introduction of a new type of services, addressing the user-focused service consumption. As a cause of this shift, the increasing saturation of everyday environments with computing devices can be identified. This saturation implies a numerical growth of computing systems and entails an increasing complexity, which negatively influences maintainability and manageability. Moreover, the shortcomings caused by the mobility of system elements, a common trait of human-centric environments, require consideration about the reliability of cooperative actions. In this paper, we present an approach that copes with complexity and dynamic while making service-oriented systems autonomous by the use of bio-inspired concepts. In particular, the aim is to make service architectures environment-aware. Thus, service architectures are supposed to adapt autonomously to their current environment like biological species do to survive. This approach requires services to obtain knowledge about characteristics and state of the environment through gathering semantically enhanced information about the context of the computing environment, which is intended to help in forming a virtual counterpart of the real world as reference for service adaptation. For this purpose, we illustrate the architecture for context provisioning in highly dynamic computing environments. As base for this architecture a middleware is introduced utilizing a loosely coupled interaction model. Moreover, a pheromone-based concept is outlined to optimize the dissemination of context data in the absence of adequate context sources.

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Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This article describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model. An inference algorithm is proposed to simulate human-like natural language understanding procedure. A new measurement, confidence, is introduced to facilitate the natural language understanding. The authors present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings.

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Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de)compositional meaning. The model's architecture—a multidimensional coordinate system based on neurophysiological models of sensory processing—proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations, and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation.

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AbstractThe present research examines the effects of body-object interaction (BOI) on eye gaze behaviour in a reading task. BOI measures perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g. cat) and a set of low BOI words (e.g. sun) were selected, matched on imageability and concreteness (as well as other lexical and semantic variables). Facilitatory BOI effects were observed: gaze durations and total fixation durations were shorter for high BOI words, and participants made fewer regressions to high BOI words. The results provide evidence of a BOI effect on non-manual responses and in a situation that taps normal reading processes. We discuss how the results (a) suggest that stored motor information (as measured by BOI ratings) is relevant to lexical semantics, and (b) are consistent with an embodied view of cognition (Wilson 2002).

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<p>The recent rapid progress in ICT technologies such as smart/intelligent sensor devices, broadband/ubiquitous networks, and Internet of everything (IoT) has advanced the penetration of sensor networks and their applications. The requirements of human daily life, security, energy efficiency, safety, comfort, and ecological, can be achieved with the help of these networks and applications. Traditionally, if we want some information on, for example, environment status, a variety of dedicated sensors is needed. This will increase the number of sensors installed and thus system cost, sensor data traffic loads, and installation difficulty. Therefore, we need to find redundancies in the captured information or interpret the semantics captured by non-dedicated sensors to reduce sensor network overheads. This paper clarifies the feasibility of recognizing human presence in a space by processing information captured by other than dedicated sensors. It proposes a method and implements it as a cost-effective prototype sensor network for a university library. This method processes CO2 concentration, originally designed to check environment status. In the experiment, training data is captured with none, one, or two subjects. The information gain (IG) method is applied to the resulting data, to set thresholds and thus judge the number of people. Human presence (none, one or two people) is accurately recognized from the CO2 concentration data. The experiments clarify that a CO2 sensor in set in a small room to check environment status can recognize the number of humans in the room with more than 70 % accuracy. This eliminates the need for an extra sensor, which reduces sensor network cost.</p>

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The rapid development of the Internet has led to introducing new methods for e-recruitment and human resources management. These methods aim to systematically address the limitations of conventional recruitment procedures through incorporating natural language processing tools and semantics-based methods. In this context, for a given job post, applicant resumes (usually uploaded as free-text unstructured documents in different formats such as .pdf, .doc or .rtf) are matched/screened out using the conventional keyword-based model enriched by additional resources such as occupational categories and semantics-based techniques. Employing these techniques has proved to be effective in reducing the cost, time, and efforts required in traditional recruitment and candidate selection methods. However, bridging the skill gap - that is, the propensity to precisely detect and extract relevant skills in applicant resumes and job posts - and highlighting the hidden semantic dimensions encoded in applicant resumes are still challenging issues in the process of devising effective e-recruitment systems. This is due to the fact that resources exploited by current e-recruitment systems are obtained from generic domain-independent sources, therefore resulting in knowledge incompleteness and the lack of domain coverage. In this article, we review state-of-the-art e-recruitment approaches and highlight recent advancements in this domain. An e-recruitment framework addressing current shortcomings through the use of multiple cooperative semantic resources, feature extraction techniques and skill relatedness measures is detailed. An instantiation of the proposed framework is proposed and an experimental validation using a real-world recruitment dataset from two employment portals demonstrates the effectiveness of the proposed approach.

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In human language the mapping between form and meaning is arbitrary, as there is no direct connection between words and the objects that they represent. However, within a given language, it is possible to recognise systematic associations that support productivity and comprehension. In this work, we focus on the consistency between orthographic forms and meaning, and we investigate how the cognitive system may exploit it to process words. We take morphology as our case study, since it arguably represents one of the most notable examples of systematicity in form–meaning mapping. In a series of three experiments, we investigate the impact of form–meaning mapping in word processing by testing new consistency metrics as predictors of priming magnitude in primed lexical decision. In Experiment 1, we re-analyse data from five masked morphological priming studies and show that orthography–semantics–consistency explains independent variance in priming magnitude, suggesting that word semantics is accessed already at early stages of word processing and that crucially semantic access is constrained by word orthography. In Experiments 2 and 3, we investigate whether this pattern is replicated when looking at semantic priming. In Experiment 2, we show that orthography–semantics–consistency is not a viable predictor of priming magnitude with longer stimulus onset asynchrony (SOA). However, in Experiment 3, we develop a new semantic consistency measure based on the semantic density of target neighbourhoods. This measure is shown to significantly predict independent variance in semantic priming effect. Overall, our results indicate that consistency measures provide crucial information for the understanding of word processing. Specifically, the dissociation between measures and priming paradigms shows that different priming conditions are associated with the activation of different semantic cohorts.

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Research into semantic similarity has a long history in lexical semantics, and it has applications in many natural language processing (NLP) tasks like word sense disambiguation or machine translation. The task of calculating semantic similarity is usually presented in the form of datasets which contain word pairs and a human-assigned similarity score. Algorithms are then evaluated by their ability to approximate the gold standard similarity scores. Many such datasets, with different characteristics, have been created for English language. Recently, four of those were transformed to Thai language versions, namely WordSim-353, SimLex-999, SemEval-2017-500, and R&G-65. Given those four datasets, in this work we aim to improve the previous baseline evaluations for Thai semantic similarity and solve challenges of unsegmented Asian languages (particularly the high fraction of out-of-vocabulary (OOV) dataset terms). To this end we apply and integrate different strategies to compute similarity, including traditional word-level embeddings, subword-unit embeddings, and ontological or hybrid sources like WordNet and ConceptNet. With our best model, which combines self-trained fastText subword embeddings with ConceptNet Numberbatch, we managed to raise the state-of-the-art, measured with the harmonic mean of Pearson on Spearman ρ, by a large margin from 0.356 to 0.688 for TH-WordSim-353, from 0.286 to 0.769 for TH-SemEval-500, from 0.397 to 0.717 for TH-SimLex-999, and from 0.505 to 0.901 for TWS-65.

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Narrowly specialized information is addressed to a limited circle of professionals though it provokes interest among people without specialized education. This gives rise to a need for the popularization of scientific information. This process is carried out through simplified texts as a kind of secondary texts that are directly aimed at the addressee. Age, language proficiency and background knowledge are the main features which are usually taken into consideration by the author of the secondary text who makes changes in the text composition, as well as in its pragmatics, semantics and syntax. This article analyses traditional approaches to text simplification, computer simplification and summarization. The authors compare human-authored simplification of literary texts with the newest trends in computer simplification to promote further development of machine simplification tools. It has been found that the samples of simplified scientific texts seem to be more natural than the samples of simplified literary texts since technical background knowledge can be processed with machine tools. The authors have come to the conclusion that literary and technical texts should imply different approaches for adaptation and simplification. In addition, personal readers’ experience plays a great part in finding the implications in literary texts. In this respect it might be reasonable to create separate engines for simplifying and adapting texts from diverse spheres of knowledge. Keywords Text Simplification; Natural Language Processing (NLP); Pragmatic Adaptation; Professional Communication; Literary Texts

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Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference.

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Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.

Abstract:

Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.

Abstract:

Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.

Abstract:

Background: Revisiting the 2008 Science article by Mitchell et al. on computational neurolinguistics, individual typological differences were found as striking characteristics in the patterns of informative voxels crucial for the distributed semantic processing system. Methods: The results of different feature selection methods (ANOVA and Stability) were compared based on the open datasets of each subject for evaluating how these features were decisive in predicting human brain activity associated with language meaning. Results: In general, the two selection results were similar and the voxel-wise ranks were correlated but they became extremely dispersive for a subgroup of subjects exhibiting mediocre precision when examined without regularization. Quite interestingly, looking at the anatomical location of these voxels, it appears that the modality-specific areas were likely to be monitored by the Stability score (indexing “identity”), and that the ANOVA (emphasizing “difference”) tended to detect supramodal semantic areas. Conclusions: This minor finding indicates that in some cases, seemingly poor data may deeply and systematically conceal information that is significant and worthwhile. It may have potential for shedding new light on in the controversy pertaining to cognitive semantics, which is divided into modality-biased (embodied) and amodal symbol theories.

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Objective. The article deals with the analysis of the trends in the logic of the semantic organization of the speech and mental activity of adolescents as regards the dimension of the available semantic environment and that of the speech competences of an individual. It is stated that information about the world is systematized by human consciousness in the form of an associative-verbal field, whose producing and modelling is one of the ways of representing the speech competence of an individual shaping his/her destiny. Materials & Methods. In view of the above, an empirical research of the distinctive features of expanding semantic content of the adolescents' speech consciousness as regards the dimension of the logic-of-semantics organization of the mental and speech activity was performed using the diagnostic potential of a free associative experiment. A system of relevant ways of construction and associative expansion of meanings in the semantic field of the speech consciousness of adolescents was chosen as the subject of the research. Results. Drawing on the results of the frequency analysis of the use of typical associative patterns for organizing the semantic field of speech, it was stated that the most represented among adolescents is a semantic way of reasoning based on the identification signs of the general contour of the trigger word at the level of its meaning. It was proved that the greatest changes in the logic of expanding associative semantic content are observed among older adolescents, which is interpreted as the result of fundamental changes in the strategies for cognitive processing of information flows. Conclusions. A conclusion was drawn about the stable tendency towards the standardization and unification of speech in adolescents during their transition to adulthood, and the reduction in the heuristic resource of the intellectual activity of older adolescent pupils was noted. In this context, the purposeful formation of speech and mental competences of an individual, the acquisition by adolescents of efficient methods of the logic-of-semantics organization of the associative semantic content of speech is defined as a necessary prerequisite for the personality development, as well as that for the speech and mental development of an individual.

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How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.

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Abstract Background and Purpose: The growing role and involvement of Artificial Intelligence in modern digital enterprises leads to a considerable reduction of personnel and reorientation of the remaining staff to new responsibilities. However, in many areas like services and support the total elimination of the employed human resources still remains impossible. It is proposed to study the organisational problem of finding the optimal proportion of computer agents and human actors in the mixed collaborative environment. Methods: Using the technology of semantic and statistical analysis, we developed an original model of computer agents’ and human actors’ cooperative interaction and an optimization method, which is novel in considering the focus of the executors while calculating the compliance indicators. Results: The problem was studied by an example of service desk automation. Considering the semantics of the problem domain in the form of ontology introduces the logic for better distribution and automation of tasks. Conclusion: In a modern digital enterprise there exists and can be estimated a rational balance between the computer agents and human actors, which becomes a significant indicator of its performance. In general, human actors are preferable for processing unpredictable events in real time, while agents are better at the modelling and simulation.

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Abstract We have two motivations. Firstly, semantic gap is a tough problem puzzling almost all sub-fields of Artificial Intelligence. We think semantic gap is the conflict between the abstractness of high-level symbolic definition and the details, diversities of low-level stimulus. Secondly, in object recognition, a pre-defined prototype of object is crucial and indispensable for bi-directional perception processing. On the one hand this prototype was learned from perceptional experience, and on the other hand it should be able to guide future downward processing. Human can do this very well, so physiological mechanism is simulated here. We utilize a mechanism of classical and non-classical receptive field (nCRF) to design a hierarchical model and form a multi-layer prototype of an object. This also is a realistic definition of concept, and a representation of denoting semantic. We regard this model as the most fundamental infrastructure that can ground semantics. Here a AND-OR tree is constructed to record prototypes of a concept, in which either raw data at low-level or symbol at high-level is feasible, and explicit production rules are also available. For the sake of pixel processing, knowledge should be represented in a data form; for the sake of scene reasoning, knowledge should be represented in a symbolic form. The physiological mechanism happens to be the bridge that can join them together seamlessly. This provides a possibility for finding a solution to semantic gap problem, and prevents discontinuity in low-order structures.

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The subject of the research is the process of collecting and preliminary preparation of data from heterogeneous sources. Economic information is heterogeneous and semi-structured or unstructured in nature. Due to the heterogeneity of the primary documents, as well as the human factor, the initial statistical data may contain a large amount of noise, as well as records, the automatic processing of which may be very difficult. This makes preprocessing dynamic input data an important precondition for discovering meaningful patterns and domain knowledge, and making the research topic relevant.Data preprocessing is a series of unique tasks that have led to the emergence of various algorithms and heuristic methods for solving preprocessing tasks such as merge and cleanup, identification of variablesIn this work, a preprocessing algorithm is formulated that allows you to bring together into a single database and structure information on time series from different sources. The key modification of the preprocessing method proposed by the authors is the technology of automated data integration.The technology proposed by the authors involves the combined use of methods for constructing a fuzzy time series and machine lexical comparison on the thesaurus network, as well as the use of a universal database built using the MIVAR concept.The preprocessing algorithm forms a single data model with the ability to transform the periodicity and semantics of the data set and integrate data that can come from various sources into a single information bank.

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Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology ; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research.

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Abstract The topic approached in this paper aims to identify the structural similarities between the verbal and the musical language and to highlight the process of decoding the musical message through the structural analogy between them. The process of musical perception and musical decoding involves physiological, psychological and aesthetic phenomena. Besides receiving the sound waves, it implies complex cognitive processes being activated, whose aim is to decode the musical material at cerebral level. Starting from the research methods in cognitive psychology, music researchers redefine the process of musical perception in a series of papers in musical cognitive psychology. In the case of the analogy between language and music, deciphering the musical structure and its perception are due, according to researchers, to several common structural configurations. A significant model for the description of the musical structure is Noam Chomsky’s generative-transformational model. This claimed that, at a deep level, all languages have the same syntactic structure, on account of innate anatomical and physiological structures which became specialized as a consequence of the universal nature of certain mechanisms of the human intellect. Chomsky’s studies supported by sophisticated experimental devices, computerised analyses and algorithmic models have identified the syntax of the musical message, as well as the rules and principles that underlie the processing of sound-related information by the listener; this syntax, principles and rules show surprising similarities with the verbal language. The musicologist Heinrich Schenker, 20 years ahead of Chomsky, considers that there is a parallel between the analysis of natural language and that of the musical structure, and has developed his own theory on the structure of music. Schenker’s structural analysis is based on the idea that tonal music is organized hierarchically, in a layering of structural levels. Thus, spoken language and music are governed by common rules: phonology, syntax and semantics. Fred Lerdahl and Ray Jackendoff develop a musical grammar where a set of generating rules are defined to explain the hierarchical structure of tonal music. The authors of the generative theory propose the hypothesis of a musical grammar based on two types of rules, which take into account the conscious and unconscious principles that govern the organization of the musical perception. The structural analogy between verbal and musical language consists of several common elements. Among those is the hierarchical organization of both fields, a governance by the same rules – phonology, syntax, semantics – and as a consequence of the universal nature of certain mechanisms of the human intellect, decoding the transmitted message is accomplished thanks to some universal innate structures, biologically inherited. Also, according to Chomsky's linguistics model a musical grammar is configured, one governed by wellformed rules and preference rules. Thus, a musical piece is not perceived as a stream of disordered sounds, but it is deconstructed, developed and assimilated at cerebral level by means of cognitive pre-existing schemes.

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AbstractSentence embeddings are the suitable input vectors for the neural learning of a number of inferences about content and meaning. Similarity estimation, classification, emotional characterization of sentences as well as pragmatic tasks, such as question answering or dialogue, have largely demonstrated the effectiveness of vector embeddings to model semantics. Unfortunately, most of the above decisions are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings. We think that any effective approach to meaning representation should be at least epistemologically coherent. In this paper, we concentrate on the readability of neural models, as a core property of any embedding technique consistent and effective in representing sentence meaning. In this perspective, this paper discusses a novel embedding technique (the Nyström methodology) that corresponds to the reconstruction of a sentence in a kernel space, inspired by rich semantic similarity metrics (a semantic kernel) rather than by a language model. In addition to being based on a kernel that captures grammatical and lexical semantic information, the proposed embedding can be used as the input vector of an effective neural learning architecture, called Kernel-based deep architectures (KDA). Finally, it also characterizes by design the KDA explanatory capability, as the proposed embedding is derived from examples that are both human readable and labeled. This property is obtained by the integration of KDAs with an explanation methodology, called layer-wise relevance propagation (LRP), already proposed in image processing. The Nyström embeddings support here the automatic compilation of argumentations in favor or against a KDA inference, in form of an explanation: each decision can in fact be linked through LRP back to the real examples, that is, the landmarks linguistically related to the input instance. The KDA network output is explained via the analogy with the activated landmarks. Quantitative evaluation of the explanations shows that richer explanations based on semantic and syntagmatic structures characterize convincing arguments, as they effectively help the user in assessing whether or not to trust the machine decisions in different tasks, for example, Question Classification or Semantic Role Labeling. This confirms the epistemological benefit that Nyström embeddings may bring, as linguistically rich and meaningful representations for a variety of inference tasks.

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From year to year the number of investigations on intelligent systems grows rapidly. For example this year 245 papers from 45 countries were sent for the Second International IEEE Conference on Intelligent Systems (www.ieee-is.org; www.fnts-bg.org/is) and this is an increase of more than 50% by all indicators. The presented papers on intelligent systems were marked by big audiences and they provoked a significant interest that ultimately led to the formation of vivid discussions, exchange of ideas and locally provoked the creation of working groups for different applied projects. All this reflects the worldwide tendencies for the leading role of the research on intelligent systems theoretically and practically. The greater part of the presented research dealt with traditional for the intelligent systems problems like artificial intelligence, knowledge engineering, intelligent agents, neural and fuzzy networks, intelligent data processing, intelligent control and decision making systems, and also new interdisciplinary problems like ontology and semantics in Internet, fuzzy intuitionistic logic. The majority of papers from the European and American researchers are dedicated to the theory and the applications of the intelligent systems with machine learning, fuzzy inference or uncertainty. Another big group of papers focuses on the domain of building and integrating ontologies of applications with heterogeneous multiagent systems. A great number of papers on intelligent systems deals with fuzzy sets. The papers of many other researchers underscore the significance of the contemporary perception-oriented methods and also of different applications in the intelligent systems. On the first place this is valid for the paradigm of L. A. Zadeh 'computing with words'. The Guest Editors in the present specialized journal volume would like to introduce a wealth of research with an applied and theoretical character that possesses a common characteristic and it is the conference best papers complemented and updated by the new elaborations of the authors during the last half a year. A short description of the presented in the volume papers follows. In 'Combining Local and Global Access to Ontologies in a Multiagent System' <B>R. Brena and H. Ceballos (Mexico)</B> proposed an original way for operation with ontologies where a part of the ontology is processed by a client's component and the rest is transmitted to the other agents by an ontology agent. The inter-agent communication is improved in this way. In 'Fuzzy Querying of Evolutive Situations: Application to Driving Situations' <B>S. Ould Yahia and S. Loriette-Rougegrez (France)</B> present an approach to analysis of driving situations using multimedia images and fuzzy estimates that will improve the driver's security. In 'Rememberng What You Forget in an Online Shopping Context' <B>M. Halvey and M. Keane (Ireland)</B> presented their approach to constructing online system that predicts the items for future shopping sessions using a novel idea called Memory Zones. In 'Reinforcement Learning for Online Industrial Process Control' the authors <B>J. Govindhasamy et al. (Ireland)</B> use a synthesis of dynamic programming, reinforcement learning and backpropagation for a goal of modeling and controlling an industrial grinding process. The felicitous combination of methods contributes for a greater effectiveness of the applications compared to the existing controllers. In 'Dynamic Visualization of Information: From Database to Dataspace' the authors <B>C. St-Jacques and L. Paquin (Canada)</B> suggested a friendly online access to large multimedia databases. <B>W. Huang (UK)</B> redefines in 'Towards Context-Aware Knowledge Management in e-Enterprises' the concept of context in intelligent systems and proposes a set of meta-information elements for context description in a business environment. His approach is applicable in the E-business, in the Semantic Web and in the Semantic Grid. In 'Block-Based Change Detection in the Presence of Ambient Illuminaion Variations' <B>T. Alexandropoulos et al. (Greece)</B> use a statistic analysis, clustering and pattern recognition algorithms, etc. for the goal of noise extraction and the global illumination correction. In 'Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results' <B>C. Chesñevar (Spain) and A. Maguitman (USA)</B> proposed a recommender system for improving the WEB search. Defeasible argumentation and decision support methods have been used in the system. In 'Modified Axiomatic Basis of Subjective Probability' <B>K. Tenekedjiev et al. (Bulgaria)</B> make a contribution to the axiomatic approach to subjective uncertainty by introducing a modified set of six axioms to subjective probabilities. In 'Fuzzy Rationality in Quantitative Decision Analysis' <B>N. Nikolova et al. (Bulgaria)</B> present a discussion on fuzzy rationality in the elicitation of subjective probabilities and utilities. The possibility to make this special issue was politely offered to the Guest Editors by Prof. Kaoru Hirota, Prof. Toshio f*ckuda and we thank them for that. Due to the help of Kenta Uchino and also due to the new elaborations presented by explorers from Europe and America the appearance of this special issue became possible.

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The purpose of the article is to determine the principles of Robert MacLaury’s Vantage Theory and to review its application in various linguistic studies.Methods, used in the paper, include analysis and description, the application of which was conditioned by the task to distinguish the main tenets of the Vantage Theory and to present experience of its application in various linguistic studies. Results. A groundbreaking theory of R. MacLaury has overcome the lack of explanatory power of cognitive grammar and the prototype theory. The starting point of the Vantage Theory is the recognition that categorization takes place on analogy of human’s orientation on terrain with regard to movement. Adapted by primitive people for information processing, the cognitive mechanism of orientation in space and time is deeply rooted in human consciousness. Any category appears in comparison with images or other types of discrete ideas that correspond to fixed space-time coordinates to which observer’s attention is drawn while detecting similarities and differences in the object of perception. The categorization model consists of three levels, which correspond to a pair of fixed and variable coordinates. A person can focus on only one pair of coordinates at a time, others are stored in memory as proven facts, which become a prerequisite for inference and an integral part of the categorization model. Apart from numerous works on colour semantics, the theory is applied to address a wide range of issues in sociolinguistic studies, discourse analysis, cognitive grammar, etc. The theory is also applicable in the study of verbalized sociocultural stereotypes. The author of the article uses the Vantage Theory in the cognitive-linguistics study of sociocultural stereotypes in American media discourse. As verbalized, conventionally evaluative patterns of social groups, sociocultural stereotypes are formed in comparison with the ethical, behavioural, and aesthetic standards of American culture that act as fixed coordinates of mental space.Conclusions. The Vantage Theory of Robert MacLaury is a further step in the development of cognitive linguistics. The universality of this theory lies in its ability to explain many linguistic facts and therefore it can be applied in the study of a wide range of issues related to the linguistic representation of knowledge, such as concepts and stereotypes, which are categories of a verbalized picture of the world.Key words: vantage theory, categorization, space-time orientation, perspectivization, sociocultural stereotype. Мета статті полягає у визначенні основних положень теорії побудови перспектив Роберта Маклорі та огляді її застосування у лінгвістичних студіях різної спрямованості.Методи дослідження включають аналіз та опис, застосування яких зумовлене завданням виділити окремі положення, представити складну для розуміння теорію, виявити можливості її пристосування для різних дослідницьких цілей у лінгвістичних дослідженнях.Результати. Вихідним положенням цієї теорії є визнання того, що категоризація проходить за глибоко вкоріненою в людську свідомість аналогією орієнтації людини на місцевості з урахуванням руху. Категорії виникають у процесі пізнавальної взаємодії з фіксованими та змінними координатами, які створюють різні перспективи, аналогічні тим, що люди використо-вують для орієнтації у фізичному просторі. Будь-яка категорія формується у зрівнянні з визначеними образами чи іншими видами дискретних ідей, при цьому увага людини фіксується на подібності та різниці до встановлених образів. Модель категоризації складається з трьох рівнів, що співвідносяться з парою фіксованої і змінної координати. Людина може концентрувати увагу тільки на одній парі координат, інші зберігаються в пам’яті як доведені факти, які стають передумовою умовиводів та невід’ємною частиною категоризаційної моделі. Теорія успішно використовується у дослідженнях дискурсу, лексичній семантиці, у соціолінгвістиці та когнітивній граматиці. Автор статті використає теорію Маклорі у лінгвокогнітивному дослідженні соціокультурних стереотипів американського медіадискурсу. Як вербалізовані конвенційно-оцінні зразки соціальних груп, соціокультурні стереотипи формуються у зрівнянні з етичними, поведінковими та естетичними стандартами американської культури, що діють як фіксовані координати ментального простору.Висновки. Теорія Маклорі є подальшим кроком у розвитку когнітивної лінгвістики. Універсальність цієї теорії полягає в тому, що вона може бути застосована у дослідженнях широкого кола питань, що стосуються мовної репрезентації знання у вербалізованих концептах та стереотипах як категоріях мовленнєвої картини світу.Ключові слова: теорія побудови перспектив, просторово-часова орієнтація, фіксовані і змінні координати, перспективізація, вербалізований соціокультурний стереотип.

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The Routledge Handbook of language and culture represents a comprehensive study on the inextricable relationship between language and culture. It is structured into seven parts and 33 chapters. Part 1, Overview and historical background, by Farzad Sharifian, starts with an outline of the book and a synopsis of research on language and culture. The second chapter, John Leavitt’s Linguistic relativity: precursors and transformations discusses further the historical development of the concept of linguistic relativity, identifying different schools’ of thought views on the relation between language and culture. He also tries to demystify some misrepresentations held towards Boas, Sapir, and Whorf’ theories (pp. 24-26). Chapter 3, Ethnosyntax, by Anna Gladkova provides an overview of research on ethnosyntax, starting from the theoretical basis laid by Sapir and Whorf and investigates the differences between a narrow sense of ethnosyntax, which focuses on cultural meanings of various grammatical structures and a broader sense, which emphasises the pragmatic and cultural norms’ impact on the choice of grammatical structures. John Leavitt presents in the fourth chapter, titled Ethnosemantics, a historical account of research on meaning across cultures, introducing three traditions, i.e. ‘classical’ ethnosemantics (also referred to as ethnoscience or cognitive anthropology), Boasian cultural semantics (linguistically inspired anthropology) and Neohumboldtian comparative semantics (word-field theory, or content-oriented Linguistics). In Chapter 5, Goddard underlines the fact that ethnopragmatics investigates emic (or culture-internal) approaches to the use of different speech practices across various world languages, which accounts for the fact that there exists a connection between the cultural values or norms and the speech practices peculiar to a speech community. One of the key objectives of ethnopragmatics is to investigate ‘cultural key words’, i.e. words that encapsulate culturally construed concepts. The concept of ‘linguaculture’ (or languaculture) is tackled in Risager’s Chapter 6, Linguaculture: the language–culture nexus in transnational perspective. The author makes reference to American scholars that first introduced this notion, Paul Friedrich, who looks at language and culture as a single domain in which verbal aspects of culture are mingled with semantic meanings, and Michael Agar, for whom culture resides in language while language is loaded with culture. Risager himself brought forth a new global and transnational perspective on the concept of linguaculture, i.e. the use of language (linguistic practice) is seen as flows in people’s social networks and speech communities. These flows enhance as people migrate or learn new languages, in permanent dynamics. Lidia Tanaka’s Chapter 7, Language, gender, and culture deals with research on language, gender, and culture. According to her, the language-gender relationship has been studied by researchers from various fields, including psychology, linguistics, and anthropology, who mainly consider gender as a construct that preserves inequalities in society, with the help of language, too. Tanaka lists diachronically different approaches to language and gender, focusing on three specific ones: gender stereotyped linguistic resources, semantically, pragmatically or lexically designated language features (including register) and gender-based spoken discourse strategies (talking-time imbalances or interruptions). In Chapter 8, Language, culture, and context, Istvan Kecskes delves into the relationship between language, culture, and context from a socio-cognitive perspective. The author considers culture to be a set of shared knowledge structures that encapsulate the values, norms, and customs that the members of a society have in common. According to him, both language and context are rooted in culture and carriers of it, though reflecting culture in a different way. Language encodes past experience with different contexts, whereas context reflects present experience. The author also provides relevant examples of formulaic language that demonstrate the functioning of both types of context, within the larger interplay between language, culture, and context. Sara Miller’s Chapter 9, Language, culture, and politeness reviews traditional approaches to politeness research, with particular attention given to ‘discursive approach’ to politeness. Much along the lines of the previous chapter, Miller stresses the role of context in judgements of (im)polite language, maintaining that individuals represent active agents who challenge and negotiate cultural as well as linguistic norms in actual communicative contexts. Chapter 10, Language, culture, and interaction, by Peter Eglin focuses on language, culture and interaction from the perspective of the correspondence theory of meaning. According to him, abstracting language and culture from their current uses, as if they were not interdependent would not lead to an understanding of words’ true meaning. David Kronenfeld introduces in Chapter 11, Culture and kinship language, a review of research on culture and kinship language, starting with linguistic anthropology. He explains two formal analytic definitional systems of kinship terms: the semantic (distinctions between kin categories, i.e. father vs mother) and pragmatic (interrelations between referents of kin terms, i.e. ‘nephew’ = ‘child of a sibling’). Chapter 12, Cultural semiotics, by Peeter Torop deals with the field of ‘semiotics of culture’, which may refer either to methodological instrument, to a whole array of methods or to a sub-discipline of general semiotics. In this last respect, it investigates cultures as a form of human symbolic activity, as well as a system of cultural languages (i.e. sign systems). Language, as “the preserver of the culture’s collective experience and the reflector of its creativity” represents an essential component of cultural semiotics, being a major sign system. Nigel Armstrong, in Chapter 13, Culture and translation, tackles the interrelation between language, culture, and translation, with an emphasis on the complexities entailed by translation of culturally laden aspects. In his opinion, culture has a double-sided dimension: the anthropological sense (referring to practices and traditions which characterise a community) and a narrower sense, related to artistic endeavours. However, both sides of culture permeate language at all levels. Chapter 14, Language, culture, and identity, by Sandra Schecter tackles several approaches to research on language, culture, and identity: social anthropological (the limits at play in the social construction of differences between various groups of people), sociocultural (the interplay between an individual’s various identities, which can be both externally and internally construed, in sociocultural contexts), participatory-relational (the manner in which individuals create their social–linguistic identities). Patrick McConvell, in Chapter 15, Language and culture history: the contribution of linguistic prehistory reviews research in this field where historical linguistic evidence is exploited in the reconstruction and understanding of prehistoric cultures. He makes an account of research in linguistic prehistory, with a focus on proto- and early Indo-European cultures, on several North American language families, on Africa, Australian, and Austronesian Aboriginal languages. McConvell also underlines the importance of interdisciplinary research in this area, which greatly benefits from studies in other disciplines, such as archaeology, palaeobiology, or biological genetics. Part four starts with Ning Yu’s Chapter 16, Embodiment, culture, and language, which gives an account of theory and research on the interplay between language, culture, and body, as seen from the standpoint of Cultural Linguistics. Yu presents a survey of embodiment (in embodied cognition research) from a multidisciplinary perspective, starting with the rather universalistic Conceptual Metaphor Theory. On the other hand, Cultural Linguistics has concentrated on the role played by culture in shaping embodied language, as various cultures conceptualise body and bodily experience in different ways. Chapter 17, Culture and language processing, by Crystal Robinson and Jeanette Altarriba deals with research in the field of how culture influence language processing, in particular in the case of bilingualism and emotion, alongside language and memory. Clearly, the linguistic and cultural character of each individual’s background has to be considered as a variable in research on cognition and cognitive processing. Frank Polzenhagen and Xiaoyan Xia, in Chapter 18, Language, culture, and prototypicality bring forth a survey of prototypicality across different disciplines, including cognitive linguistics and cognitive psychology. According to them, linguistic prototypes play a critical part in social (re-)cognition, as they are socially diagnostic and function as linguistic identity markers. Moreover, individuals may develop ‘culturally blended concepts’ as a result of exposure to several systems of conceptual categorisation, especially in the case of L2 learning (language-contact or culture-contact situations). In Chapter 19, Colour language, thought, and culture, Don Dedrick investigates the issue of the colour words in different languages and how these influence cognition, a question that has been addressed by researchers from various disciplines, such as anthropology, linguistics, cognitive psychology, or neuroscience. He cannot but observe the constant debate in this respect, and he argues that it is indeed difficult to reach consensus, as colour language occasionally reveals effects of language on thought and, at other times, it is impervious to such effects. Chapter 20, Language, culture, and spatial cognition, by Penelope Brown concentrates on conceptualisations of space, providing a framework for thinking about and referring to objects and events, along with more abstract notions such as time, number, or kinship. She lists three frames of reference used by languages in order to refer to spatial relations, i.e. a) an ‘absolute’ coordinate system, like north, south, east, west; b) a ‘relative’ coordinate system envisaged from the body’s standpoint; and c) an intrinsic, object-centred coordinate system. Chris Sinha and Enrique Bernárdez focus on, in Chapter 21, Space, time, and space–time: metaphors, maps, and fusions, research on linguistic and cultural concepts of time and space, starting with the seminal Conceptual Metaphor Theory (CMT), which they denounce for failing to situate space–time mapping within the broader patterns of culture and world perspective. Sinha and Bernárdez further argue that although it is possible in all cultures for individuals to experience and discuss about events in terms of their duration and succession, the specific words and concepts they use to refer to temporal landmarks temporal and duration are most of the time language and culture specific. Chapter 22, Culture and language development, by Laura Sterponi and Paul Lai provides an account of research on the interplay between culture and language acquisition. They refer to two widely accepted perspectives in this respect: a developmental mechanism inherent in human beings and a set of particular social contexts in which children are ‘initiated’ into the cultural meaning systems. Both perspectives define culture as “both related to the psychological make-up of the individual and to the socio-historical contexts in which s/he is born and develops”. Anna Wierzbicka presents, in Chapter 23, Language and cultural scripts discusses representations of cultural norms which are encoded in language. She contends that the system of meaning interpretation developed by herself and her colleagues, i.e. Natural Semantic Metalanguage (NSM), may easily be used to capture and convey cultural scripts. Through NSM cross-cultural experiences can be captured in a thorough manner by using a reduced number of conceptual primes which seem to exist in all languages. Chapter 24, Culture and emotional language, by Jean-Marc Dewaele brings forth the issue of the relationship between language, culture, and emotion, which has been researched by cultural and cognitive psychologists and applied linguists alike, although with some differences in focus. He considers that within this context, it is important to see differences between emotion contexts in bilinguals, since these may lead to different perceptions of the self. He infers that generally, culture revolves around the experience and communication of emotions, conveyed through linguistic expression. The fifth part starts with Chapter 25, Language and culture in sociolinguistics, by Meredith Marra, who underlines that culture is a central concept in Interactional Sociolinguistics, where language is considered as social interaction. In linguistic interaction, culture, and especially cultural differences are deemed as a cause of potential miscommunication. Mara also remarks that the paradigm change in sociolinguistics, from Interactional Sociolinguistics to social constructionism reshaped ‘culture’ into a more dynamic as well as less rigid concept. Claudia Strauss’ Chapter 26, Language and culture in cognitive anthropology deals with the relationship between human society and human thought/thinking. The author contends that cognitive anthropologists may be subdivided into two groups, i.e. ones that are concerned with the process of thinking (cognition-in-practice scholars), and the others focusing on the product of thinking or thoughts (concerned with shared cultural understandings). She goes on to explore how different approaches to cognitive anthropology have counted on units of language, i.e. lexical items and their meanings, along with larger chunks of discourse, as information, which may represent learned cultural schemata. Part VI starts with Chapter 27, Language and culture in second language learning, by Claire Kramsch, in which she makes a survey of the definition of ‘culture’ in foreign language learning and its evolution from a component of literature and the arts to a more comprehensive purport, that of culturally appropriate use of language, along with an appropriate use of sociopragmatic and pragmalinguistic norms. According to her, in the postmodern era, communication is not only mere transmission of information, it represents construal and positioning of the self and of self-identity. Chapter 28, Writing across cultures: ‘culture’ in second language writing studies, by Dwight Atkinson focuses on the usefulness of culture in second-language writing (SLW). He reviews several approaches to the issue: contrastive rhetoric (dealing with the impact of first-language patterns of text organisation on writers in a second language), or even alternate notions, like‘ cosmopolitanism’, ‘critical multiculturalism’, and hybridity, as of late native culture is becoming irrelevant or at best far less significant. Ian Malcolm tackles, in Chapter 29, Language and culture in second dialect learning, the issue of ‘standard’ Englishes (e.g., Standard American English, Standard Australian English) versus minority ‘non-standard’ speakers of English. He deplores the fact that in US specialist literature, speaking the ‘non-standard’ variety of English was associated with cognitive, cultural, and linguistic insufficiency. He further refers to other specialists who have demonstrated that ‘non-standard’ varieties can be just as systematic and highly structured as the standard variety. Chapter 30, Language and culture in intercultural communication, by Hans-Georg Wolf gives an account of research in intercultural education, focusing on several paradigms, i.e. the dominant one, investigating successful functioning in intercultural encounters, the minor one, exploring intercultural understanding and the ‘deconstructionist, and or postmodernist’. He further examines different interpretations of the concepts associated with intercultural communication, including the functionalist school, the intercultural understanding approach and a third one, the most removed from culture, focusing on socio-political inequalities, fluidity, situationality, and negotiability. Andy Kirkpatrick’s Chapter 31, World Englishes and local cultures gives a synopsis of research paradigm from applied linguistics which investigates the development of Englishes around the world, through processes like indigenisation or nativisation of the language. Kirkpatrick discusses the ways in which new Englishes accommodate the culture of the very speech community which develops them, e.g. adopting lexical items to express to express culture-specific concepts. Speakers of new varieties could use pragmatic norms rooted in cultural values and norms of the specific new speech community which have not previously been associated with English. Moreover, they can use these new Englishes to write local literatures, often exploiting culturally preferred rhetorical norms. Part seven starts with Chapter 32, Cultural Linguistics, by Farzad Sharifian gives an account of the recent multidisciplinary research field of Cultural Linguistics, which explores the relationship between language and cultural cognition, particularly in the case of cultural conceptualisations. Sharifian also brings forth illustrations of how cultural conceptualisations may be linguistically encoded. The last chapter, A future agenda for research on language and culture, by Roslyn Frank provides an appraisal of Cultural Linguistics as a prospective path for research in the field of language and culture. She states that ‘Cultural Linguistics could potentially create a paradigm that “successfully melds together complementary approaches, e.g., viewing language as ‘a complex adaptive system’ and bringing to bear upon it concepts drawn from cognitive science such as ‘distributed cognition’ and ‘multi-agent dynamic systems theory’.” She further asserts that Cultural Linguistics has the potential to function as “a bridge that brings together researchers from a variety of fields, allowing them to focus on problems of mutual concern from a new perspective” and most likely unveil new issues (as well as solutions) which have not been evident so far. In conclusion, the Handbook will most certainly serve as clear and coherent guidelines for scholarly thinking and further research on language and culture, and also open up new investigative vistas in each of the areas tackled.

Abstract:

The works of many linguists view hom*onymy as a negative phenomenon, which interferes with communication, complicates the perception of information, and decreases the effectiveness of the language as a means of communication. At the same time hom*onymy is a positive phenomenon which contributes to the compactness of the language, and allows to economize the units of the plain of content. The objective of our research is to determine the factors that differentiate the meaning of hom*onymic units, based on the broad factual material and psycholinguistic experiments. The components of intralinguistic hom*onymic rows based on the category of markedness, which correlates with the cognitive operator of norm / deviation. Among the criteria of markedness for hom*onymic differentiation are areal, social, chronological, and stylistic. The fact that one of the elements of the hom*onymic row is unmarked was proved by a number of psycholinguistic experiments, where we offered the German speakers to suggest the first association word which occurred to them referring the hom*onyms in the list. The experiment was carried out in a group of students from the Institute of German Studies, Technical University Chemnitz (Germany), aged 21-25, whose native language is German. The psycholinguistic analysis shows that 97 per cent of hom*onymic pairs have both marked and unmarked components. This allows to explain hom*onymy from the point of view of the correlation of “markedness/unmarkedness”, and wider – “norm/deviation”. From the cognitive point of view language markedness is derived from cognitive markedness, i.e. the unmarked language meaning corresponds to the cognitively normal (natural, expected) state of things, and the marked language meaning corresponds to cognitive deviation, i.e. unnatural, unexpected state of things. Normal state of things belongs to the cognitive image of human experience, and is conceptualized with the minimal mental calculating effort, i.e. is activated automatically; and deviations from this image require additional calculating resources for their activation. Thus, language markedness reflects cognitive operators of norm/deviation in the specific language means in language structures, including hom*onymic pairs and hom*onymic rows. References Bridges, R.S. (2004). On English hom*ophones. Society for Pure English Tract 02. Oxford: Clarendon Pr. Retrieved from: http://www.gutenberg.org/files/14227/14227-h/14227-h.htm (01.05.2020) Cairns, H.S. (1973). Effects of bias on processing and reprocessing of lexically ambi­guous sentences. Journal of Experimental Psychology, 97, 337-343. Croft, W. (2003). Typology and universals. 2-d ed. Cambridge: Cambridge University Press. Ducháček, O. (1953). O vzájemném vlivu tvaru a významu slov. Praha: Státní peda­go­gické nakladatelství. Ferreira, L. (2010). Metaphor comprehension in foreign language. In L. Scliar-Cabral, (Ed). Psycholinguistics: Scientific and Technological Challenges. (pp. 84-98). Porto Alegre: EDIPUCRS, Foss, D.J. & Jenkins C.M. (1973). Some effects of context on the comprehension of ambiguous sentences. Journal of Verbal Learning and Verbal Behavior, 12, 577-589. Greenberg, J. (1966). Language universals, with special reference to feature hie­rarchies. The Hague: Mouton. Hogaboam, T.W. & Perfetti, C.A. (1975). Lexical ambiguity and sentence compre­hension. Journal of Verbal Learning and Verbal Behaviour, 14, 265-274. Кибрик, А.Е. (2008). Лингвистическая реконструкция когнитивной структуры. Вопросы языкознания. 4, 51-77. Кійко, С.В. (2016). Синергетика омонімії як мовного, мовленнєвого і міжмовного явища. Чернівці: Родовід. Mаулер, Ф.И. (1983). Грамматическая омонимия в современном английском языке. Ростов: Изд. Рост. университета. Новиков, Л.А. (1982). Семантика русского языка. М.: Высшая Школа. Реформатский, А.А. (2004). Введение в языкознание. 5 изд. М.: Аспект Пресс Press. Ruoff, A. (2014). Häufigkeitswörterbuch gesprochener Sprache. 2. Auflage. Berlin: de Gruyter. Reprint 2014. DOI: https://doi.org/10.1515/9783110916058 References (translated and transliterated) Bridges, R.S. (2004). On English hom*ophones. Society for Pure English Tract 02. Oxford: Clarendon Pr. Retrieved from: http://www.gutenberg.org/files/14227/14227-h/14227-h.htm (01.05.2020) Cairns, H.S. (1973). Effects of bias on processing and reprocessing of lexically ambi­guous sentences. Journal of Experimental Psychology, 97, 337-343. Croft, W. (2003). Typology and universals. 2-d ed. Cambridge: Cambridge University Press. Ducháček, O. (1953). O vzájemném vlivu tvaru a významu slov. Praha: Státní peda­go­gické nakladatelství. Ferreira, L. (2010). Metaphor comprehension in foreign language. In L. Scliar-Cabral, (Ed). Psycholinguistics: Scientific and Technological Challenges. (pp. 84-98). Porto Alegre: EDIPUCRS. Foss, D.J. & Jenkins C.M. (1973). Some effects of context on the comprehension of ambiguous sentences. Journal of Verbal Learning and Verbal Behavior, 12, 577-589. Greenberg, J. (1966). Language universals, with special reference to feature hie­rarchies. The Hague: Mouton. Hogaboam, T.W. & Perfetti, C.A. (1975). Lexical ambiguity and sentence compre­hension. Journal of Verbal Learning and Verbal Behaviour, 14, 265-274. Kibrik, А.Е. (2008). Lingvističeskaya rekonstrukciya kognitivnoy struktury [Linguistic reconstruction of cognitive structure]. Voprosy yazykoznaniya, 4, 51–77. Kiyko, S.V. (2016). Synergetyka omonimii yak movnoho, movlennevoho i moshmovnoho yavyshcha. [Synergy of hom*onymy as language, speech and interlanguage phenomenon]. Chernivtsi: Rodovid. Mauler, F.I. (1983). Grammatičeskaya omonimiya v sovremennom angliyskom yazyke [Grammatical hom*onymy in Modern English]. Rostov: Rostov University Publishers. Novikov, L.А. (1982). Semantika russkogo yazyka [Semantics of Russian language]. М.: Vys. shkola. Reformatskiy, А.А. (2004). Vvedeniye v yazykoznaniye [Introduction into linguistics]. 5-th ed. Мoscow: Aspekt Press. Ruoff, A. (2014). Häufigkeitswörterbuch gesprochener Sprache. 2. Auflage. Berlin: de Gruyter. Reprint. https://doi.org/10.1515/9783110916058.

Abstract:

The nature of semantic representations of plural nouns has been a subject of debates in the literature. The present research investigated the extent to which there are differences in the processing of plural versus single noun descriptions (e.g., the large chairs vs. the large chair). In two reading experiments, we tested whether plural (versus singular) nouns appearing in sentences were more difficult to process initially and/or led to increased processing difficulty when occurring in sentences that contain a temporary syntactic ambiguity. Reading time on syntactically ambiguous sentences containing plural or singular nouns were compared with reading time on unambiguous control sentences. The results of both experiments demonstrated significant effects of sentence ambiguity. No effects or interactions involving noun number were observed, indicating that the complexity of plural nouns does not result in processing difficulty during sentence comprehension. References Adams, B., Clifton, C., & Mitchell, D. (1998). Lexical guidance in sentence processing? Psychonomic Bulletin and Review, 5(2), 265-270. Baayen, R. H., Dijkstra, T., & Schreuder, R. (1997). Singulars and plurals in Dutch: Evidence for a parallel dual-route model. Journal of Memory and Language, 37(1), 94-117. Barker, C. (1992). Group Terms in English: Representing Groups as Atoms. Journal of Semantics 9, 69-93. Barsalou, L. W. (1999). Perceptual symbol systems. Brain and Behavioral Sciences, 22, 577-660. Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning & Verbal Behavior, 12, 335-359. Dominguez, A., Cuetos, F., & Segui, J. (1999). The processing of grammatical gender and number in Spanish. Journal of Psycholinguistic Research, 28(5), 485-498. Ferreira, F., & Henderson, J. (1990). Use of verb information during syntactic parsing: Evidence from eye tracking and word by word self-paced reading. Journal of Experimental Psychology: Learning, Memory, & Cognition, 16, 555-568. Ferreira, F., & Henderson, J. M. (1991). Recovery from misanalyses of garden-path sentences. Journal of Memory and Language, 30(6), 725-745. Ferreira, F., & McClure, K. K. (1997). Parsing of garden-path sentences with reciprocal verbs. Language and Cognitive Processes, 12, 273–306. Garnsey, S. M., Pearlmutter, N. J., Myers, E., & Lotocky, M. (1997). The contributions of verb bias and plausibility to the comprehension of temporarily ambiguous sentences. Journal of Memory & Language, 37, 58-93. Johnson-Laird, P. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press. Kennison, S. M. (2001). Limitations on the use of verb information in sentence comprehension. Psychonomic Bulletin & Review, 8, 132-138. Kennison, S. M. (2005). Different time courses of integrative semantic processing for plural and singular nouns: Implications for theories of sentence processing. Cognition, 97, 269-294. Mitchell, D. C. (1987). Lexical guidance in human parsing: Locus and processing characteristics. In M. Coltheart (Ed.), Attention and Performance 12: The psychology of reading (pp. 601-618). Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc. New, B., Brysbaert, M., Segui, J., Ferrand, L., & Rastle, K. (2004). The processing of singular and plural nouns in French and English. Journal of Memory and Language, 51, 568-585. Patson, N. D. (2014). The processing of plural expressions. Language and Linguistics Compass, 8(8), 319-329. doi:http://dx.doi.org/10.1111/lnc3.12085 Patson, N. D., George, G., & Warren, T. (2014). The conceptual representation of number. The Quarterly Journal of Experimental Psychology, 67(7), 1349-1365. doi:http://dx.doi.org/10.1080/17470218.2013.863372 Patson, N.(2014). The processing of plural expressions. Language and Linguistics Compass, 8(8), 319-329. doi:http://dx.doi.org/10.1111/lnc3.12085 Patson, N. (2016). Evidence in support of a scalar implicature account of plurality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(7), 1140-1153. doi:10.1037/xlm0000224 Patson, N. D., & Ferreira, F. (2009). Conceptual plural information is used to guide early parsing decisions: Evidence from garden-path sentences with reciprocal verbs. Journal of Memory and Language, 60, 464-486. Patson, N., George, G., & Warren, T. (2014). The conceptual representation of number. The Quarterly Journal of Experimental Psychology, 67(7), 1349-1365. doi:http://dx.doi.org/10.1080/17470218.2013.863372 Patson, N., & Warren, T. (2011). Building complex reference objects from dual sets. Journal of Memory and Language, 64, 443–459. Patson, N., & Warren, T. (2014). Comparing the roles of referents and event structures in parsing preferences. Language, Cognition and Neuroscience, 29, 408–423. http://dx.doi.org/10.1080/01690965.2013.788197 Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime (Version 2.0). [Computer software and manual]. Pittsburgh, PA: Psychology Software Tools Inc. Schreuder, R., & Baayen, R. (1997). How complex simple words can be. Journal of Memory and Language, 37(1), 118-139. Schwarzschild, R. (1996). Pluralities. Kluwer, Dordrecht. Sereno, J. A., & Jongman, A. (1997). Processing of English inflectional morphology. Memory & Cognition, 25(4), 425-437. Sturt, P., Pickering, M. J., & Crocker, M. W. (2000). Search strategies in syntactic reanalysis. Journal of Psycholinguistic Research, 29(2), 183-194. Zwaan, R. A., Stanfield, R. A., & Yaxley, R. H. (2002). Language comprehenders mentally represent the shapes of objects. Psychological science, 13(2), 168-171.

Abstract:

An ongoing debate in Chinese psycholinguistics is whether subject-relative clauses or object-relative clauses are more difficult to process. The current study asks what happens when structure and plausibility are pitted against each other in Chinese relative clause processing. Chinese relative clause structures and semantic plausibility were manipulated to create both plausible and implausible versions of subject- and object-relative clauses. This method has been used in other languages (e.g., English) to elicit thematic role reversal comprehension errors. Importantly, these errors—as well as online processing difficulties—are especially frequent in implausible versions of dispreferred (noncanoncial) structures. If one relative clause structure in Chinese is highly dispreferred, the structural factor and plausibility factor should interact additively. If, however, the structures are relatively equally difficult to process, then there should be only a main effect of plausibility. Sentence reading times as well as analyses on lexical interest areas revealed that Chinese readers used plausibility information almost exclusively when reading the sentences. Relative clause structure had no online effect and small but consistent offline effects. Taken together, the results support a slight preference in offline comprehension for Chinese subject-relative clauses, as well as a central role for semantic plausibility, which appears to be the dominant factor in online processing and a strong determinant of offline comprehension.

Abstract:

AbstractSign language offers a unique perspective on the human faculty of language by illustrating that linguistic abilities are not bound to speech and writing. In studies of spoken and written language processing, lexical variables such as, for example, age of acquisition have been found to play an important role, but such information is not as yet available for German Sign Language (Deutsche Gebärdensprache, DGS). Here, we present a set of norms for frequency, age of acquisition, and iconicity for more than 300 lexical DGS signs, derived from subjective ratings by 32 deaf signers. We also provide additional norms for iconicity and transparency for the same set of signs derived from ratings by 30 hearing non-signers. In addition to empirical norming data, the dataset includes machine-readable information about a sign’s correspondence in German and English, as well as annotations of lexico-semantic and phonological properties: one-handed vs. two-handed, place of articulation, most likely lexical class, animacy, verb type, (potential) hom*onymy, and potential dialectal variation. Finally, we include information about sign onset and offset for all stimulus clips from automated motion-tracking data. All norms, stimulus clips, data, as well as code used for analysis are made available through the Open Science Framework in the hope that they may prove to be useful to other researchers: 10.17605/OSF.IO/MZ8J4

Abstract:

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Abstract:

Question Answer (QA) System for Reading Comprehension (RC) is a computerized approach to retrieve relevant response to the query posted by the users. The underlined concept in developing such a system is to build a human computer interaction. The interactions will be in natural language and we tend to use negation words as a part of our expressions. During the pre-processing stage in Natural Language Processing (NLP) task these negation words gets removed and hence the semantics gets changed. This remains to be an unsolved problem in QA system. In order to maintain the semantics we have proposed a novel approach Hybrid NLP based Bi-directional Long Short Term Memory (Bi-LSTM) with attention mechanism. It deals with the negation words and maintains the semantics of the sentence. We also focus on answering any factoid query (i.e. ’what’, ’when’, ’where’, ’who’) that is raised by the user. For this purpose, the use of attention mechanism with softmax activation function has obtained superior results that matches the question type and process the context information effectively. The experimental results are performed over the SQuAD dataset for reading comprehension and the Stanford Negation dataset is used to perform the negation in the RC sentence. The accuracy of the system over negation is obtained as 93.9% and over the QA system is 87%.

Abstract:

Objective: One key task of fine-grained opinion mining on product review is to extract product aspects and their corresponding opinion expressed by users. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. Recent years have seen a revival of the long short-term memory (LSTM), with its effectiveness being demonstrated on a wide range of problems. However, LSTM based approaches are still limited to linear data processing since it processes the information sequentially. As a result, they may perform poorly on user-generated texts, such as product reviews, tweets, etc., whose syntactic structure is not precise.To tackle this challenge, <P> Methods: In this research paper, we propose a constituency tree long short term memory neural network-based approach. We compare our model with state-of-the-art baselines on SemEval 2014 datasets. <P> Results: Experiment results show that our models obtain competitive performances compared to various supervised LSTM architectures. <P> Conclusion: Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results.

Abstract:

Chemical compounds (drugs) and diseases are among top searched keywords on the PubMed database of biomedical literature by biomedical researchers all over the world (according to a study in 2009). Working with PubMed is essential for researchers to get insights into drugs’ side effects (chemical-induced disease relations (CDR), which is essential for drug safety and toxicity. It is, however, a catastrophic burden for them as PubMed is a huge database of unstructured texts, growing steadily very fast (~28 millions scientific articles currently, approximately two deposited per minute). As a result, biomedical text mining has been empirically demonstrated its great implications in biomedical research communities. Biomedical text has its own distinct challenging properties, attracting much attetion from natural language processing communities. A large-scale study recently in 2018 showed that incorporating information into indenpendent multiple-input layers outperforms concatenating them into a single input layer (for biLSTM), producing better performance when compared to state-of-the-art CDR classifying models. This paper demonstrates that for a CNN it is vice-versa, in which concatenation is better for CDR classification. To this end, we develop a CNN based model with multiple input concatenated for CDR classification. Experimental results on the benchmark dataset demonstrate its outperformance over other recent state-of-the-art CDR classification models. Keywords: Chemical disease relation prediction, Convolutional neural network, Biomedical text mining References [1] Paul SM, S. Mytelka, C.T. Dunwiddie, C.C. Persinger, B.H. Munos, S.R. Lindborg, A.L. Schacht, How to improve R&D productivity: The pharmaceutical industry's grand challenge, Nat Rev Drug Discov. 9(3) (2010) 203-14. https://doi.org/10.1038/nrd3078. [2] J.A. DiMasi, New drug development in the United States from 1963 to 1999, Clinical pharmacology and therapeutics 69 (2001) 286-296. https://doi.org/10.1067/mcp.2001.115132. [3] C.P. Adams, V. Van Brantner, Estimating the cost of new drug development: Is it really $802 million? Health Affairs 25 (2006) 420-428. https://doi.org/10.1377/hlthaff.25.2.420. [4] R.I. Doğan, G.C. Murray, A. Névéol et al., "Understanding PubMed user search behavior through log analysis", Oxford Database, 2009. [5] G.K. Savova, J.J. Masanz, P.V. Ogren et al., "Mayo clinical text analysis and knowledge extraction system (cTAKES): Architecture, component evaluation and applications", Journal of the American Medical Informatics Association, 2010. [6] T.C. Wiegers, A.P. Davis, C.J. Mattingly, Collaborative biocuration-text mining development task for document prioritization for curation, Database 22 (2012) pp. bas037. [7] N. Kang, B. Singh, C. Bui et al., "Knowledge-based extraction of adverse drug events from biomedical text", BMC Bioinformatics 15, 2014. [8] A. Névéol, R.L. Doğan, Z. Lu, "Semi-automatic semantic annotation of PubMed queries: A study on quality, Efficiency, Satisfaction", Journal of Biomedical Informatics 44, 2011. [9] L. Hirschman, G.A. Burns, M. Krallinger, C. Arighi, K.B. Cohen et al., Text mining for the biocuration workflow, Database Apr 18, 2012, pp. bas020. [10] Wei et al., "Overview of the BioCreative V Chemical Disease Relation (CDR) Task", Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, 2015. [11] P. Verga, E. Strubell, A. McCallum, Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction, In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1 (2018) 872-884. [12] Y. Shen, X. Huang, Attention-based convolutional neural network for semantic relation extraction, In: Proceedings of COLING 2016, the Twenty-sixth International Conference on Computational Linguistics: Technical Papers, The COLING 2016 Organizing Committee, Osaka, Japan, 2016, pp. 2526-2536. [13] Y. Peng, Z. Lu, Deep learning for extracting protein-protein interactions from biomedical literature, In: Proceedings of the BioNLP 2017 Workshop, Association for Computational Linguistics, Vancouver, Canada, 2016, pp. 29-38. [14] S. Liu, F. Shen, R. Komandur Elayavilli, Y. Wang, M. Rastegar-Mojarad, V. Chaudhary, H. Liu, Extracting chemical-protein relations using attention-based neural networks, Database, 2018. [15] H. Zhou, H. Deng, L. Chen, Y. Yang, C. Jia, D. Huang, Exploiting syntactic and semantics information for chemical-disease relation extraction, Database, 2016, pp. baw048. [16] S. Liu, B. Tang, Q. Chen et al., Drug–drug interaction extraction via convolutional neural networks, Comput, Math, Methods Med, Vol (2016) 1-8. https://doi.org/10.1155/2016/6918381. [17] L. Wang, Z. Cao, G. De Meloet al., Relation classification via multi-level attention CNNs, In: Proceedings of the Fifty-fourth Annual Meeting of the Association for Computational Linguistics 1 (2016) 1298-1307. https://doi.org/10.18653/v1/P16-1123. [18] J. Gu, F. Sun, L. Qian et al., Chemical-induced disease relation extraction via convolutional neural network, Database (2017) 1-12. https://doi.org/10.1093/database/bax024. [19] H.Q. Le, D.C. Can, S.T. Vu, T.H. Dang, M.T. Pilehvar, N. Collier, Large-scale Exploration of Neural Relation Classification Architectures, In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 2266-2277. [20] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, In Proceedings of the IEEE. 86(11) (1998) 2278-2324. [21] Y. Kim, Convolutional neural networks for sentence classification, ArXiv preprint arXiv:1408.5882. [22] C. Nagesh, Panyam, Karin Verspoor, Trevor Cohn and Kotagiri Ramamohanarao, Exploiting graph kernels for high performance biomedical relation extraction, Journal of biomedical semantics 9(1) (2018) 7. [23] H. Zhou, H. Deng, L. Chen, Y. Yang, C. Jia, D. Huang, Exploiting syntactic and semantics information for chemical-disease relation extraction, Database, 2016.

Abstract:

ObjectiveWhile there is a growing torrent of data that disease surveillance could leverage, few effective tools exist to help public health professionals make sense of this data or that provide secure work-sharing and communication. Meanwhile, our ever more-connected world provides an increasingly receptive environment for diseases to emerge and spread rapidly making early warning and collaborative decision-making essential to saving lives and reducing the impact of outbreaks. Digital Infuzion's previous work on the Defense Threat Reduction Agency (DTRA)'s Biosurveillance Ecosystem (BSVE) built a cloud-based platform to ingest big data with analytics to provide users a robust surveillance environment. We next enhanced the BSVE data sources and analytics to support an integrated One Health paradigm. The resulting BSVE and Digital Infuzion's HARBINGER platform include: 1) identifying and ingesting data sources that span global human, animal and crop health; 2) inclusion of non-health data such as travel, weather, and infrastructure; 3) the data science tools, analytics and visualizations to make these data useful and 4) a fully-featured Collaboration Center for secure work-sharing and communication across agencies.IntroductionAfter the 2009 H1N1 pandemic, the Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense indicated “biodefense” would include emerging infectious disease. In response, DTRA launched an initiative for an innovative, rapidly emerging capability to enable real-time biosurveillance for early warning and course of action analysis. Through competitive prototyping, DTRA selected Digital Infuzion to develop the platform and next generation analytics. This work was extended to enhance collaboration capabilities and to harness data science and advanced analytics for multi-disciplinary surveillance including climate, crop, and animal as well as human data. New analysis tools ensure the BSVE supports a One Health paradigm to best inform public health action. Digital Infuzion and DTRA first introduced the BSVE to the ISDS community at the 2013 annual conference SWAP Meet. Digital Infuzion is pleased to present the mature platform to this community again as it is now a fully developed capability undergoing FedRAMP certification with the Department of Homeland Security’s National Biosurveillance Integration Center and Is the basis for Digital Infuzion's HARBINGER ecosystem for biosurveillance.MethodsWe integrated over 170 global One Health data sources using cloud-based automated data ingestion workflows that provide unified access with data provenance. We used modular automated workflows to implement data science including Natural Language Processing (NLP), machine learning, anomaly detection, and expert systems for extraction of concepts from unstructured text. A first of its kind ontology for biosurveillance permits linking of data across sources. This ontology allows users to rapidly find all relevant data by looking at semantic relationships within and across data sets having varying quality, types, and usages to understand the best, most complete indicators of impending threats.We applied the following principles to the development of data science tools: 1) mathematics should be fully automated and operate 'under the hood' without need for user intervention; 2) 'At-a-Glance' visualizations should summarize Information, draw attention to key aspects and permit drill down into underlying data; 3) data science analytics and tools need to be validated with real-world data and by disease surveillance experts and 4) secure collaboration capabilities are essential to biosurveillance activities.This was a highly complex effort. We worked closely with surveillance analysts from multiple agencies and organizations to continuously guide the development of capabilities. We drew upon subject matter expertise in public health, machine learning, social media, NLP, semantics, big data integration, computational science, and visualization. A high level of automation, security and immediacy of data was applied to support rapid identification and investigation of potential outbreaks.ResultsThe platform now provisions integrated One Health information. Data sources were harmonized and expanded, along with historical information, to better predict and understand biothreats. These include global social media, human, plant, animal, and weather data. An Analyst Workbench delivers logical, intuitive and interactive visualizations enabling disease surveillance professionals to identify critical, predictive information without extensive manual research. Over 700 approved users currently have access to the prototype.Biosurveillance activities can be performed collaboratively among governmental agencies, public health officials, and the general public using the Collaboration Center and its sharing and messaging systems. Data sharing is HIPAA compliant and distinguishes public from private data using carefully controlled and approved role- and attribute-based access for security.To speed disease surveillance workflows, the workbench generates suggestions to the user on their current work. Anomaly detection to alert to potential developing disease events employs fully automated analytics to conduct over 43 million calculations daily for more than 500 diseases in over 170 data sources, distilling this into a table that ranks the most significant anomalous increases that may indicate an outbreak and warrant investigation.A predictive disease modeling tool based on current and historical data uses fuzzy logic to identify the likeliest outcome, even early in an outbreak when there is much uncertainty about the disease and its characteristics. A complex automated workflow identifies health-related topics that are trending in Twitter and evaluates their severity using novel lexicons and new reactive sentiment analysis. Searches use the ontology to gather all relevant information and are supported by the most advanced NLP with custom surveillance rules to provide succinctly extracted information. This alleviates the need for extensive reading by identifying exactly which data is needed and extracting key concepts from it. Intuitive methods of visual representation, interactive displays, and drill-down capabilities were leveraged in all analytics for rapid understanding of results.Finally, we added a software development kit to enable third party developers to continuously enhance the platform capabilities by adding new data sources and new analytic apps. This allows the platform to be adapted for specific needs and to keep pace with new scientific and technical discoveries and has resulted in over 50 analytic apps.ConclusionsThe addition of One Health data and analytics, and the integration of health data with unconventional data sources and modern approaches to data science and complex workflows, resulted in enhanced situational awareness and decision-making capabilities for users. The expanded Collaboration Center within the workbench, enables users to partner and collaborate with other agencies and biosurveillance professionals both nationally and internationally to maximize the rapidity of responses to serious disease outbreaks.

Abstract:

>> See video of presentation (28 min.) The primary goal of scholarly communication is improving human knowledge and sharing is the key to achieve this goal: sharing ideas, sharing methodologies, sharing of results, sharing data, information and knowledge. Although the concept of sharing applies to all phases of scholarly communication, most often the only visible part is the final publication, with the journal article as a most common type. The traditional characteristics of the present journals allow only limited possibilities for sharing the knowledge. Basic functions, registration, dissemination, certification, and storage, are still present but they are no more effective in the network environment. Registration is too slow, there are various barriers to dissemination, certification system has many shortcomings, and used formats are not suitable for the long term preservation and storage. Although the journals today are digital and various powerful technologies are available, they are still focused on their unaltered printed versions. This presentation will discuss possible evolution of journal article to become more compliant with users' needs and to enable “the four R’s of openness” – reuse, redistribute, revise and remix (Hilton, Wiley, Stein, & Johnson, 2010).Several aspects of openness will be presented and discussed: open access, open data, open peer review, open authorship, and open formats. With digital technology which has become indispensable in the creation, collection, processing and storage of data in all scientific disciplines the way of conducting scientific research has changed and the concept of "data-driven science" has been introduced (Ware & Mabe, 2009). Sharing research data enhances the capabilities of reproducing the results, reuse maximizes the value of research, accelerating the advancement of science, ensuring transparency of scientific research, reducing the possibility of bias in the interpretation of results and increasing the credibility of published scientific knowledge. The open peer review can ensure full transparency of the entire process of assessment and help to solve many problems in the present scholarly publishing. Through the process of the open peer review each manuscript can be immediately accessible, reviewers can publicly demonstrate their expertise and could be rewarded, and readers can be encouraged to make comments and views and to become active part of the scholarly communication process. The trend to to describe the author's contribution is also present, which will certainly lead to a reduced number of “ghost”, "guest" and "honorary" authors, and will help to establish better standards for author’s identification.Various web technologies can be used also for the semantic enhancement of the article. One of the most important aspects of semantic publication is the inclusion of the research data, to make them available to the user as an active data that can be manipulated. It is possible to integrate data from external sources, or to merge the data from different resources (data fusion) (Shotton, 2012), so the reader can gain further understanding of the presented data. Additional options provide merging data from different articles, with the addition of the component of time. Other semantic enhancement can include enriched bibliography, interactive graphical presentations, hyperlinks to external resources, tagged text, etc.Instead of mostly static content, journals can offer readers dynamic content that includes multimedia, "living mathematics", “executable articles”, etc. Videos highlighting critical points in the research process, 3D representations of chemical compounds or art works, audio clips with the author's reflections and interviews, and animated simulations or models of ocean currents, tides, temperature and salinity structure, can became soon common part of every research article. The diversity of content and media, operating systems (GNU / Linux, Apple Mac OSX, Microsoft Windows), and software tools that are available to researchers, suggests the usage of the appropriate open formats. Different formats have their advantages and disadvantages and it would be necessary to make multiple formats available, some of which are suitable for "human" reading (including printing on paper), and some for machine reading that can be used by computers without human intervention. Characteristics and possibilities of several formats will be discussed, including XML as the most recommended format, which can enable granulate document structure as well as deliver semantics to the human reader or to the computer.Literature:Hilton, J. I., Wiley, D., Stein, J., & Johnson, A. (2010). The Four R’s of Openness and ALMS Analysis: Frameworks for Open Educational Resources. Open Learning: The Journal of Open, Distance and E-Learning, 25(1), 37–44. doi:10.1080/02680510903482132Shotton, D. (2012). The Five Stars of Online Journal Articles - a Framework for Article Evaluation. D-Lib Magazine, 18(1/2), 1–16. doi:10.1045/january2012-shottonWare, M., & Mabe, M. (2009). The stm report (p. 68).