Language Representation Research Articles

The Role of the Motor System in L1 and L2 Action Verb Processing for Chinese Learners of English: Evidence from Mu Rhythm Desynchronization.

The nature of semantic representation has long been a key question in linguistic research. The Embodied Cognition theory challenges the traditional view of language representation, stating that semantic information stems from the sensory-motor cortex, which is activated automatically during semantic processing. However, most of the evidence comes from monolingual studies; it remains unclear whether second-language (L2) comprehension involves different semantic representations or mirrors the pattern seen in first-language (L1) processing. Therefore, the present study investigated the role of the sensory-motor system in language processing via making Electroencephalography (EEG) recordings during the processing of L1 and L2 action verbs. The results showed that L1 (Chinese) action verbs generated higher mu-event-related desynchronization (ERD) than L1 abstract verbs in the early processing stage (250 ms after verb presentation), and the same phenomenon was also observed for L2 (English). The results also indicated that language modulated the processing of action verbs, with L1 action verbs eliciting stronger ERD than L2 action verbs. These results demonstrate that the sensory-motor cortex plays a crucial role in comprehending both L1 and L2 action verbs.

Leveraging language representation for materials exploration and discovery

Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems.

Языковые способы экспликации прецедентных феноменов: сохранение и передача культурной информации (на материале художественного произведения англоязычного автора С. Моэма “Of Human Bondage”)

The article is an analysis and generalization of the language representation of precedent religious phenomena in the English language. The aim of the research is to identify the features of the linguistic-cultural explication of precedent phenomena based on artistic texts. The scientific novelty lies in the actualization of the valuable constants of precedent phenomena correlated with national culture. The work details the ways of representing precedent phenomena at the linguistic level using linguistic-cultural analysis of key verbalizers. The mechanisms of translating the value-semantic content of precedent phenomena are described, and the linguistic means of objectifying precedent phenomena with a religious orientation are identified. The analysis of examples confirms the pragmatic potential of metaphor, antonomasia, and allusion as effective means of explicating conceptual representations of spiritual concepts embedded in the semantic field of precedentiality. The conducted research was based on the artistic work "Of Human Bondage" by S. Maugham.

Extending the Architecture of Language From a Multimodal Perspective.

Language is inherently multimodal. In spoken languages, combined spoken and visual signals (e.g., co-speech gestures) are an integral part of linguistic structure and language representation. This requires an extension of the parallel architecture, which needs to include the visual signals concomitant to speech. We present the evidence for the multimodality of language. In addition, we propose that distributional semantics might provide a format for integrating speech and co-speech gestures in a common semantic representation.

Construction grammar and procedural semantics for human-interpretable grounded language processing

Abstract Grounded language processing is a crucial component in many artificial intelligence systems, as it allows agents to communicate about their physical surroundings. State-of-the-art approaches typically employ deep learning techniques that perform end-to-end mappings between natural language expressions and representations grounded in the environment. Although these techniques achieve high levels of accuracy, they are often criticized for their lack of interpretability and their reliance on large amounts of training data. As an alternative, we propose a fully interpretable, data-efficient architecture for grounded language processing. The architecture is based on two main components. The first component comprises an inventory of human-interpretable concepts learned through task-based communicative interactions. These concepts connect the sensorimotor experiences of an agent to meaningful symbols that can be used for reasoning operations. The second component is a computational construction grammar that maps between natural language expressions and procedural semantic representations. These representations are grounded through their integration with the learned concepts. We validate the architecture using a variation on the CLEVR benchmark, achieving an accuracy of 96 %. Our experiments demonstrate that the integration of a computational construction grammar with an inventory of interpretable grounded concepts can effectively achieve human-interpretable grounded language processing in the CLEVR environment.

Network bullying detection based on deep learning

The rise of social networking in today’s society has brought convenience to people’s lives, but at the same time people are also suffering from cyberbullying. How to check these bullying languages has become a popular problem. As text is an important vehicle for online social networking, the natural language learning, representation, and training becomes a necessary work for cyberbullying detection. In this paper, we summarize and analyze the existing work by studying it, and then finally propose new ideas and experiments. The specific method is based on the LSTM model, in which the parameters and dimensions are adjusted to demonstrate the best results of the model. And a user rating system is used to detect bullying more effectively.

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

DEVELOPMENT OF AN ONTOLOGICAL MODEL OF DEEP LEARNING NEURAL NETWORKS

This research paper examines the challenges and prospects associated with the integration of artificial neural networks and knowledge bases. The focus is on leveraging this integration to address practical problems. The paper explores the development, training, and integration of artificial neural net- works, emphasizing their adaptation to knowledge bases. This adaptation involves processes such as in- tegration, communication, representation of ontological structures, and interpretation by the knowledge base of the artificial neural network's representation through input and output. The paper also delves into the direction of establishing an intellectual environment conducive to the development, training, and integration of adapted artificial neural networks with knowledge bases. The knowledge base embedded in an artificial neural network is constructed using a homogeneous semantic network, and knowledge processing employs a multi-agent approach. The representation of artificial neural networks and their specifications within a unified semantic model of knowledge representation is detailed, encompassing text-based specifications in the language of knowledge representation with theoretical semantics. The models shared with the knowledge base include dynamic and other types that vary in their capabilities for knowledge representation. Furthermore, the paper conducts an analysis of approaches to creating artificial neural networks across various libraries of the high-level programming language Python. It explores techniques for developing arti- ficial neural networks within the Python development environment, investigating the key features and func- tions of these libraries. A comparative analysis of neural networks created in object-oriented programming languages is provided, along with the development of an ontological model for deep learning neural net- works.

Sprachenvielfalt von Bratislava. Eine Linguistic Landscape-Analyse am Beispiel der Michalská- und der Zelená-Straße

The empirical study presents the quantitative representation of languages in the investigated areas – Michalská and Zelena Street – in the linguistic landscape of Bratislava. At the same time, it emphasizes the necessity of further qualitative data analysis with regard to the perception of individual languages – present in the language landscape under study – by a specific type of recipient. Important evaluation criteria in the analysis process are (1) the visual accessibility of the sign, (2) the rate of distribution of the sign in the investigated area and (3) the classification of the sign (top-down vs. bottom-up signs).

A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)

Natural language processing (NLP) has significantly transformed in the last decade, especially in the field of language modeling. Large language models (LLMs) have achieved SOTA performances on natural language understanding (NLU) and natural language generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in language models. In this paper, we examine the different aspects of language models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pretraining objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different finetuning and in-context learning techniques used in downstream tasks. Moreover, it explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM’s capabilities and raised new challenges. As part of our study, we also inspect LLMs from the perspective of scalability to see how their performance is affected by the model’s depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stands.

Using word embeddings for immigrant and refugee stereotype quantification in a diachronic and multilingual setting

Word embeddings are efficient machine-learning-based representations of human language used in many Natural Language Processing tasks nowadays. Due to their ability to learn underlying word association patterns present in large volumes of data, it is possible to observe various sociolinguistic phenomena in the embedding semantic space, such as social stereotypes. The use of stereotypical framing in discourse can be detrimental and induce misconceptions about certain groups, such as immigrants and refugees, especially when used by media and politicians in public discourse. In this paper, we use word embeddings to investigate immigrant and refugee stereotypes in a multilingual and diachronic setting. We analyze the Danish, Dutch, English, and Spanish portions of four different multilingual corpora of political discourse, covering the 1997–2018 period. Then, we measure the effect of sociopolitical variables such as the number of offences committed and the size of the refugee and immigrant groups in the host country over our measurements of stereotypical association using the Bayesian multilevel framework. Our results indicate the presence of stereotypical associations towards both immigrants and refugees for all 4 languages, and that the immigrants are overall more strongly associated with the stereotypical frames than refugees.

The Power of Context: A Novel Hybrid Context-Aware Fake News Detection Approach

The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorporating auxiliary information. Our approach is based on the utilisation of Bidirectional Encoder Representations from Transformers (BERT) to capture contextualised semantic knowledge. Additionally, we employ a multichannel Convolutional Neural Network (mCNN) integrated with stacked Bidirectional Gated Recurrent Units (sBiGRU) to jointly learn multi-aspect language representations. This enables our model to effectively identify valuable clues from news content while simultaneously incorporating content- and context-based cues, such as user posting behaviour, to enhance the detection of fake news. Through extensive experimentation on four widely used real-world datasets, our proposed framework demonstrates superior performance (↑3.59% (PolitiFact), ↑6.8% (GossipCop), ↑2.96% (FA-KES), and ↑12.51% (LIAR), considering both content-based features and additional auxiliary information) compared to existing state-of-the-art approaches, establishing its effectiveness in the challenging task of fake news detection.

Religious Conflict Between Israeli and Hamas: Naming of Weapons and Battles

This research underscores the critical role of language representations in warfare, showing how naming can be a robust instrument in psychological war and narrative construction. The paper analyzes the naming practices for weaponry and battles in the Israeli-Hamas conflict, stressing the extensive use of religious and ideological symbolism by both parties. Tracking the historical and cultural contexts behind these conventions, the researchers highlight the great impact of naming on the conflict as a whole. Studying the various names given to weapons and wars, the paper unveils the deeply-rooted religious ideologies and political discourse used by Israel and Hamas. The findings reveal that naming conventions are not random, rather they are strategic tools that aim at legitimizing and obtaining support, as well as reflecting the conflict's sociopolitical dimensions. The study also found that naming practices contributes to constructing identities and justifying actions in the Israeli-Hamas conflict, illustrating the interaction of physical and symbolic elements in military and ideological strategies. In conclusion, the study provides a new perspective on the interdependent relationship between language, history, religion, and warfare.

A Systemic Functional Linguistic Analysis of Spousal Conflictual Language in a Nigerian Play

This paperx-rayed language use drawn from excerpt of utterances by the spouses (characters) in the play, Clark’s The Wives’ Revolt anchoring on the Hallidayan Systemic functional linguisticas the theoretical framework known for analysing language structure and function. The purpose of this study is to reveal the experiential representation of conflictual language of the spouses in J.P. Clark’s Wives’ Revoltsthrough its transitivity processes. Twenty – nine (29) excerpts that depict the spousal conflictual lexico-grammatical forms are purposively selected from these plays after thorough readings and they are represented accordingly for easier referencing during analysis in order to determine the frequencies of the transitivity features, their roles, functions, and meanings. A qualitative method is used for data analysis. Results from descriptive analysis showed that spouses applied four (material, mental relational and verbal processes) out of six Hallidayan’s transitivity process types to express their experiences/actions feelings for each other, negative image of themselves, and their central point of argument during their conflictual interactivity. The result showed that experiential is construed through dominance of material process, followed by mental, relational, and verbal. The prevalence of the material is found to be as a result of spouses’ more of physical actions in their relations. Again, transitive verb which transfers action to an object is also found more prevalent and is used more by the male spouse. This reveals male dominance of power over his female counterpart. The occurrence of the transitive processes will help the reader to agree with the kernel structure of the conflicting issues.

Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.

This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, we aimed to understand the specific theory-driven factors contributing to the ML prediction of suicide risk. The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors-based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk-Bert; SR-BERT) was trained using natural language processing techniques incorporating the SRF lexicon. The results showed that SR-BERT outperformed the other models. Logistic regression analysis identified several theory-driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self-harm, and thwarted belongingness. The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non-professionals in metal health. This study highlights the potential of how ML models combined with theory-driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention.

Development of Theoretical Approaches to Modern Document Studies in Linguistic School of Professor Sofiya P. Lopushanskaya

The article presents fundamental positions of the linguistic school by Sofiya P. Lopushanskaya, Professor of Volgograd State University, and describes their theoretical advancement as being applied in the discourse studies on "Document text: history and modern state". It explains that the study of business and administrative communication should be based on a clarified definition of the term document as its main constituent. The major goal of documental linguistics is to process a diverse linguistic analysis, discover objective knowledge and reason linguistic comprehension of the documental nature of communication. Within the framework of this review, some general principles of the document text analysis are presented. Firstly, they are statements on: the unity of the speech-making process approach; the interaction and coordination between language phenomena and speech production processes; extralinguistic determination of the modern document. Secondly, these statements were particularized in the linguistic school of Volgograd State University with the aim of introducing basic approaches to the study of the document text. They involve identifying the research object and its characteristics, analyzing a diverse range of texts to assess the genre and style productivity of the document text equally, and defining linguistic categories that are manifested in textual and lingual specificity. The article presents renewed facts on discursive and linguistic features of modern documents united by such text properties as consistency, structure, locality, peculiarities of the language representation of the author and addressee. It is noted that the text of the document can contain information of various qualitative types (factual, conceptual, subtextual), and it is the main constituent of the documental system and the business and administrative communication act. As the text reflects an objective and social reality, its compositional structure and semantic realization greatly depend on extralinguistic constrains. The profiles of the author (addresser) and addressee are presented as being reconstructed with the detailed observation of multi-level lingual and discursive means. The variability of the addressee profiles (the document text addressability) is presented in models of lexical, style, and text adaptation. The principles and methods of detailed description of the documet text specified in the article open up new opportunities and directions for studying document typology and distinguishing relevant features of the document communication.

Structural representation in the native language after extended second-language immersion: Evidence from acceptability judgment and memory-recall

Abstract Knowing the sentence structures (i.e., information that guides the assembly of words into sentences) is crucial in language knowledge. This knowledge must be stable for successful communication, but when learning another language that uses different structures, speakers must adjust their structural knowledge. Here, we examine how newly acquired second language (L2) knowledge influences first language (L1) structure knowledge. We compared two groups of Korean speakers: Korean-immersed speakers living in Korea (with little English exposure) versus English-immersed speakers who acquired English late and were living in the US (with more English exposure). We used acceptability judgment and sentence production tasks on Korean sentences in English and Korean word orders. Results suggest that acceptability and structural usage in L1 change after exposure to L2, but not in a way that matches L2 structures. Instead, L2 exposure might lead to increased difficulties in the selection and retrieval of word orders while using L1.

An inter-semiotic analysis of ideational meaning in text-prompted AI-generated images

Abstract This paper explores the inter-semiotic analysis of the ideational meaning in images generated by the text-to-image AI tool, Bing Image Creator. It adopts Kress and Van Leeuwen’s Grammar of Visual Design as its theoretical framework as the original grounding of the framework in systemic functional grammar (SFG) ensures a solid theoretical basis for undertaking analyses that involve the incorporation of textual and visual components. The integration of an AI generative model within the analytical framework enables a systematic connection between language and visual representations. This incorporation offers the potential to generate well-regulated pictorial representations that are systematically grounded in controlled textual prompts. This approach introduces a novel avenue for re-examining inter-semiotic processes, leveraging the power of AI technology. The paper argues that visual representations possess unique structural devices that surpass the limitations of verbal or written communication as they readily accommodate larger amounts of information in contrast to the limitations of the linear nature of alphabetic writing. Moreover, this paper extends its contribution by critically evaluating specific aspects of the Grammar of Visual Design.

A SAJÁTOS NEVELÉSI IGÉNYŰ GYEREKEK REPREZENTÁCIÓJA A KORTÁRS GYEREKKÖNYVEKBEN – AZ ŐRANGYAL, A SIMI ÉS A RENDETLEN BETŰK, VALAMINT A MIHÁLYI CSONGOR ÉS AZ IDŐGYURMA CÍMŰ KÖTETEK PÉLDÁJÁN

The present study focuses on contemporary children's books that reflect some form of special educational need. Since recently there has been an increase in the number of books on topic available in the children's book market, with publishing series, issuing the difficult life situations and on the presentation of ’otherness’, including learning disabilities, it is of great importance to take a glance at these publications. In this essay, my aim is to focus on how the texts are written and the language of representation. In particular, I will pay attention to the illustrations, which make a crucial part of the experience of reception and interpretation. With this analysis, my aim is to show how concretely or abstractly a particular educational need can be represented, whether it reinforces stereotypes or not, and how this may impact the connection to the phenomenon. The analysis of the texts also provides an opportunity to validate different attitudes, behaviours, and perspectives, and can thus contribute to sensitisation. The presentation of specific educational needs, with its positive or negative connotations, and the phenomenon of positive discrimination, are closely linked to methodological possibilities and practical implications of interpreting the texts. Here it is worthcalling attention to the concepts of helpful books, sensitisation and prevention, the relationship between these terms and literature, and reflecting on the close connection of the aesthetically shaped texts to these problems. Accordingly, the paper will first aim to explore the concept of special educational needs, then it will present its types to point out the difficulties of definition, the different approaches, and to define its own terminology. Similarly, this paper will take into account the terminology of literary studies and the perspectives relevant to this thesis. This will be followed by the analyses of the three preselected volumes which on the one hand ask whether the books are appropriate for the age group they are aimed at, while, on the other hand, they concern the possible directions for future methodological inquiries. Finally, the conclusion part summerises the results of the study and the controversies surrounding the presentation of the subject in children's books.

МОДУСНАЯ КАТЕГОРИЯ ПРОГНОЗИРОВАНИЯ (PREDICTION) И ЕЕ ИНТЕРПРЕТИРУЮЩИЙ ПОТЕНЦИАЛ

The article discusses linguistic representation of human knowledge about future events, processes, situations. The study focuses on prospective categorization of the reality. More specifically the paper considers linguistic conceptualization and categorization of forecasting as a cognitive activity. The author states that modus category of PREDICTION involves cognitive operations of interpretation, modalization, and evaluation of the conceptual content. The paper discusses discursive construal of future climate changes in English media. The findings obtained may be helpful for further study of the modal categorization as one of the forms of knowledge representation in language and its interpretative potential.