Model Of Language Research Articles

Law and Economics of Language Model Development: Empirical Examination of Corporate Strategies and Vaporware Claims

Abstract This research investigates the application of corporate strategies in developing Large Language Models (LLMs) like ChatGPT, with a focus on law and economic aspects. Improved LLM performance is largely credited to expanded dataset size, leading to developments of similar models in non-English languages. This study questions whether such announcements, particularly in the Japanese market, could be classified as ‘vaporware’ posing potential antitrust issues. Using a stock event approach, the research scrutinizes the possibility of vaporware characteristics in these announcements by examining any resultant cumulative abnormal returns (CARs). The empirical evidence suggests an absence of significant upsurges in CARs in response to these announcements, implying a lack of vaporware characteristics and a stable market response. Consequently, in a market displaying reasonable efficiency towards investment in LLM development, these findings underscore the necessity for meticulous contemplation of competition policy regulation and the implementation of industrial policy promotion measures.

Development of patient-specific 3D printed implants for total knee arthroplasty

Aim: Arthritis is a degenerative condition characterized by the progressive deterioration of the knee joint, leading to aches, rigidity, and decreased mobility. Total knee arthroplasty (TKA) surgery is performed to alleviate pain for restoring activity in these patients. TKA is carried out due to natural wear of the cartilage and meniscus or by sudden impact at the knee joint area. The surgical procedure involves careful planning, precise bone cuts, and insertion of artificial components made of metal alloys and high-density polyethylene. However, conventional manufacturing of customized knee implants involves time and cost. This work aims to present the application of three-dimensional (3D) printing for developing individualized knee implants for TKA and the challenges faced during it. Methods: Morphometry of the knee joint varies among different populations, including Indian and Western, which pose challenges during the surgery as accurate alignment and implant sizing are crucial for optimal outcomes. A female patient’s pre-surgery computed tomography (CT) scan is considered to identify the disease and to find region of interest (ROI) such as knee joint. Process involves converting scanned data to a file format for 3D printing via computer-aided design (CAD). Results: The patient’s CT scan data is processed to obtain the CAD models of knee joint and standard triangulation language (STL) file. Additional geometries and noise present near the region are removed to get ROI. Open loops and overlapping triangles are rectified in the STL file. Based on the morphometry of the bone, resection is done to obtain the CAD models of knee implants. 3D printing of the knee joint and implant prototypes is then obtained using fused deposition modelling (FDM). Line layers on the printed implant prototype are seen. Conclusions: Patient-specific 3D printed knee joint implant prototypes are successfully obtained using FDM. Challenges faced during the work are successfully worked out.

Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams

Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams

Role of English Language as a Global and Economical Language Around the Globe: A Case of Kachru's Model

This paper attempts to examine 'World Englishes' (WE) with connectivity to English as an International Language (EIL), Applied Linguistics and socio-linguistics. In the light of Kachru's model of English Language in the late 20th century. This model has three circles, inner circle, where English is used as native language, Outer Circle, mostly former colonies of British Empire, such as Singapore, India, Kenya, Ghana, Malaysia, Pakistan and others, and 3rd is Expanding Circle, include countries in which English is known as Foreign Language in schools and universities, mostly for communication and business or economic purposes as well with Inner and Outer circles. The term "English language" refers to various interesting and notable features, patterns, or aspects of the English language. These phenomena can encompass a wide range of linguistic phenomena, including grammar, vocabulary, pronunciation, syntax, idioms, and more. English holds significant importance around the world because English is the most widely spoken language globally. It serves as a common language of communication among people from different linguistic backgrounds. Proficiency in English enables individuals to connect with a broader range of people, both in personal and professional contexts. English is the language of international business and economics as well. It facilitates global trade, negotiations, and collaboration between companies and individuals from different countries. Proficiency in English enhances employability and career opportunities, particularly in multinational corporations and industries with international reach. It recognizes the importance of both native and non-native varieties of English and acknowledges that each circle has its own linguistic norms, purposes, and language development. The study informs us that Kachru was an original thinker not in the field of English Language including applied linguistics, multilingualism, bilingualism, language policy, language creativity, code mixing, code switching, cross-cultural communication, sociolinguistics but also in the domain of politics of language and so many other issues including cross-cultural awareness.

An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm

An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm

Conversion in Ossetian Language (Iron and Digor Dialects)

This article analyzes the converted lexicon of the Iron and Digor dialects of the Ossetian language. The study uses lexical units extracted through a random sampling method from Ossetian language dictionaries. The relevance of the research lies in identifying the primary conversion models necessary for solving problems related to the differentiation of parts of speech in the Ossetian language. The novelty of the study is that it attempts a scientific description of Ossetian conversion models using linguistic material from the Iron and Digor dialects. The study reveals that the most active forms of conversion in the Ossetian language are substantivization, adjectivization, and adverbialization (the direction of productivity may vary). The study also shows that the conversion model in the Ossetian language often includes nouns, adjectives, and participles. Conversion relationships between autosynthetic and synsemantic words in the Ossetian language are mainly represented by adverbs and postpositions, which are due to the developed system of postpositions in the Ossetian language. Analysis of linguistic material from the two main dialects of the Ossetian language suggests that there are no significant differences in the mechanisms of converting Iron and Digor lexicons.

Система дискурсивных понятий тайваньской реальности. Опыт создания краткого толкового словаря

The purpose of the study is to present a model for understanding foreign language (Taiwan Guoyu) discursive reality, aimed at Russian sinologists and, more widely, native speakers of the Russian language, based on a potentially expandable glossary (minimal at this stage) of basic concepts (topics of communication). Scientific novelty: for the first time, a minimum set of communication concepts of the linguistic environment of Taiwan was substantiated and formed (as a short glossary, explanatory dictionary). The previous studies described only “general” Chinese realities and values supposed to be transferred by default to the Chinese world as a whole. A comparative analysis and description of concepts with a focus on decomposition into denotative, significative and connotative components was carried out for the first time. As a result, the specificity of the basic discursive concepts of Taiwanese reality has been identified, including differences from synonymous/homonymous concepts in Russian and/or Mainland discursive reality, a list of concepts with interpretations has been proposed, and the prerequisites have been created for further study and the formation of a new direction in explorations.

Word Play in Modern Russian Network Discourse

The phenomenon of the word play is studied in this work on the basis of the network discourse reflected in the materials of the Russian competition “Word of the Year” for 2007-2021. The participants of this contest were subject to sociolinguistic analysis – words, expressions, and phrases that are products of a word play. All of them, as it turned out, were included in the nomination “Neologism of the Year”. The empirical analysis was subordinated to the goal of determining their functionality and identifying the underlying word-formation models and techniques of lexical implementation of the latter. The results of the study indicate the active appeal of users of social networks to the word play in two situations. Firstly, when a communicator wants to express one’s disagreement, a critical attitude towards real events. Then s/he usually resorts to ridicule as a form of word play, functionally aimed at lowering evaluative meanings (a case of “corrective humor”). Secondly, in a situation of counteracting psychological tension and stress caused by a traumatic event, when using a joke, the communicator seeks to reduce emotional tension—his/her own and / or the audience (a case of “benevolent humor”). In both situations, as the analysis showed, the same set of word-formation models of the Russian language and word play techniques are used. To create new lexical units, paronymic attraction and contamination are most widely used. These models are adjoined by prefixation, suffixation, their combination, as well as other traditional Russian language models for generating lexical units. Among the techniques of the word play that provide a laughing reaction, deceived expectation, logical inconsistency and playing with set expressions stand out. In general, the results of the study indicate that the word play remains a popular means of achieving the comic effect desired by the communicator, not only by journalists, as noted by analysts of modern Russian public discourse, but also by ordinary users of social networks in their everyday speech communication.

The consideration of semantic gap between design and code

In project management on creation of program application, specialists from different subject areas are involved, who include their contributions, for instance, UI/UX designers who create mock-ups of the future application or developers who write the code according to the prototype. The design conception may go beyond the possibilities of interpreting it from a technical point of view of implementation. The realization of such idea could not to be able to collect on only one defined program platform or language, and accordingly the problem is appeared. To eliminate semantic gap between the designers’ concepts and opportunity of program developers in technical affordance, released methodology, Model Driven Architecture (MDA), which is, on the one hand, a concept for implementation of software, on the other hand a standard. In paper, considerate the MDA and its transformation levels with determine a pragmatical semantics of mapping, reasons of chosen a class diagram as model of transformation and Java language for code generation.

Parallel-Based Corpus Annotation for Malay Health Documents

Named entity recognition (NER) is a crucial component of various natural language processing (NLP) applications, particularly in healthcare. It involves accurately identifying and extracting named entities such as medical terms, diseases, and drug names, and healthcare professionals are essential for tasks like clinical text analysis, electronic health record management, and medical research. However, healthcare NER faces challenges, especially in Malay, in which specialized corpora are limited, and no general corpus is available yet. To address this, the paper proposes a method for constructing an annotated corpus of Malay health documents. The researchers leverage a parallel source that contains annotated entities in English due to the limited tools available for the Malay language, and it is very language-dependent. Additional credible Malay documents are incorporated as sources to enhance the development. The targeted health entities in this research include penyakit (diseases), simptom (symptoms), and rawatan (treatments). The primary objective is to facilitate the development of NER algorithms specifically tailored to the healthcare domain in the Malay language. The methodology encompasses data collection, preprocessing, annotation of text in both English and Malay, and corpus creation. The outcome of this research is the establishment of the Malay Health Document Annotated Corpus, which serves as a valuable resource for training and evaluating NLP models in the Malay language. Future research directions may focus on developing domain-specific NER models, exploring alternative algorithms, and enhancing performance. Overall, this research aims to address the challenges of healthcare NER in the Malay language by constructing an annotated corpus and facilitating the development of tailored NER algorithms for the healthcare domain.

Even deeper problems with neural network models of language.

We recognize today's deep neural network (DNN) models of language behaviors as engineering achievements. However, what we know intuitively and scientifically about language shows that what DNNs are and how they are trained on bare texts, makes them poor models of mind and brain for language organization, as it interacts with infant biology, maturation, experience, unique principles, and natural law.

Beyond the limitations of any imaginable mechanism: Large language models and psycholinguistics.

Large language models (LLMs) are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: Providing a model for language. For this reason LLMs are important in psycholinguistics: They are useful as a practical tool, as an illustrative comparative, and philosophically, as a basis for recasting the relationship between language and thought.

Когнитивный потенциал естественных языков агглютинативного типа в интеллектуальных технологиях

This article describes an approach to development of new intelligent technologies based on the study of cognitive potential and semiotic modeling of lexical and grammatical structures of agglutinative languages (using the Tatar language as an example). A classification of cognitive lexical grammatical structures that determine the information technological effectiveness of a language is proposed. Specific examples show the relevant capabilities of the Tatar language for compact representation of knowledge, operating with fuzzy information, revealing such important properties as regularity, natural and constructive complexity, recursion, deep morphological ellipsis, functional diversity and semantic multivalence of affixes. The proposed approach, based on semiotic models of agglutinative language and the use of technological means of verbalization and contextual recognition of language meanings, can provide effective solutions for creating intelligent tools for accumulating and processing knowledge. Work in this direction creates the prospect of building a cognitive-communicative artificial intelligence system based on a universal language of communication between artificial intelligence systems as well as with humans.

GENITIVE-NEGATIVE AS THE UNIVERSAL FORM IMPLEMENTING THE ‘ABSENCE’ CONCEPT

Background. The article deals with the problem of a case category, case typology, and partial case meanings definition which is still topical. Its solution has been proposed by L. Tenier, W. Chafe, Ch. Fillmore and other scientists. The history of linguistics testifies to the fact that even Panini in his Aṣṭādhyāyī paid attention to verb-noun ratios. The case category (case – from the Latin casus < from the Greek word πτόσισ ‘fall’) is the representation of cognitive role of a name in the syntactic construction. 
 The purpose of the study is to demonstrate universality of the specific case segment – Genetive of Negation – in the context of model structures implementing the ‘Absence’ concept. Objectives are as follows: 1) to analyze the current case concepts; 2) to define reasons why case forms are used in their relation to the expressed meanings; and 3) to generate models with genitive of negation within their structure.
 Methods. A continuous sampling method has been applied to search and accumulate the required study units. A comparative method has been applied to define integral and differential characteristics of typical models in different languages. Certain elements of a comparative historical method have helped demonstrate universality of cognitive and model structures expressing the absence concept. A structural method has become the basic one while developing the models representing actual mental constructs.
 Discussion. In the study, we focused on the Genitive Case forms, i.e. on the formal Genitive Case as well as its involvement in the structures able to express absence meaning of somebody or something. Fundamentally, the Genitive Case forms in the Ukrainian and English languages are universal not only to express relation of a person/subject, called either noun or pronoun, to some another object of reality but also to express negation. In grammars, such a form is assumed to be termed as genitive-negative. 
 Conclusions. We believe that any case meaning inclusive of Genitive of Negation semantics is a result of mental activity of the specific native speakers. In the process of historical development, a meaning adapts a number of implementation forms to itself. A language remains those ones being the most reasonable from the viewpoint of articulation as well as cognitive acceptance of the image-concept-meaning-form phenomenon by a community. The capacity to agree words in a speech flow, i.e. capacity to adjust word forms to each other according to the laws of one or another language depending upon specific semantic situations is the genetic feature of human mind able to transform into verbal / lingual experience. The situation is also typical for the studied segment of the forms and meanings. All components of the listed models, supplementing a form of Genitive Case of a noun or pronoun, make together Genitive of Negation forms. Such structures are universal for the two studied languages (i.e. Ukrainian and English) while expressing the ‘Absence’ concept. At the same time, we observe universal and original Genetive of Negation models.

BERTs of a feather: Studying inter- and intra-group communication via information theory and language models.

When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in "big data." We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis.

Scaling deep learning for materials discovery

Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing1–11. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation12–14. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies15–17, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity.

Online learning and language

Online learning is a well-established mode for tertiary education and training. However, technology-mediated learning at a distance continues to face challenges to participant engagement, despite the proliferation of models of effective online learning and the implementation of increasingly sophisticated technologies to enhance learning (Bragg, Walsh & Heyeres, 2021). The critical need to build and maintain relationships for effective learning continues to be confounded by the geographic and temporal distribution of online participants as well as the nuances of the technology in use. Online learning occurs in collaborative text-spaces within learning management systems, social media spaces and their attendant learning objects such as forums, many of which foreground language as the primary resource for making meaning with others. As such, ongoing challenges to engagement in online learning may be viewed with fresh eyes by considering the language-based interpersonal affordances of these text-spaces. This pecha kucha presentation examines how the language choices made by learning facilitators impact on student engagement in online learning. It draws on a case study of the strategic use of evaluative language – the language used to express feelings and build relationships - by one teacher educator to engage initial teacher education students in online learning. Underpinned by the Systemic Functional Linguistic model of language (Halliday & Matthiessen, 2014) and following Martin & White (2005), appraisal analysis of weekly forum posts across three iterations of a unit of study shows how changes to language choices made by the teacher educator positively impacted on student engagement. This illuminates the relationship between online learning and language, and how judicious use of language-based meaning making resources can be used to improve online participant engagement.

Applying a Developmental Model to Preliterate Aided Language Learning.

Childhood spoken language interventions and augmentative and alternative communication (AAC) interventions share a common purpose: maximizing communication and language outcomes. To ensure that interventions for children who require AAC also address expressive language acquisition, this clinical focus article focuses on how to apply a developmental model of language acquisition to guide AAC decision making for preliterate aided communicators, with a particular focus on vocabulary selection. A brief review of early expressive language development is presented, along with arguments for why relying on a developmental model to guide AAC decision making is so critical. A series of detailed examples of how to apply a developmental model to various AAC vocabulary selection approaches are provided, including analyses of how well each approach aligns with pragmatic, semantic, grammatical, and narrative development. No single AAC approach for preliterate AAC language learners adequately addresses both immediate and longer-term expressive language needs; every approach has both strengths and weaknesses. Clinical decision making requires an analysis of each approach to ensure that AAC service delivery teams clearly understand the inevitable linguistic gaps, with plans put into place to fill in those gaps with different approaches. Future efforts to improve preliterate AAC service provision should use a developmental model of language as a starting point, in combination with input from families, educators, and clinicians to ensure the feasibility of the chosen approaches.

Improving domain-specific neural code generation with few-shot meta-learning

Context:Neural code generation aims to automatically generate code snippets guided by Natural Language Descriptions (NLDs). In recent years, various neural code generation models for mainstream Programming Languages (PLs), such as Java and Python, have been proposed and demonostrated significant success in prior studies. Nonetheless, due to the scarcity of available training examples for some domain-specific PLs, such as Solidity, Bash, and Clojure, simply adopting previous neural models may lead to overfitting and inadequate learning. Objective:To overcome this challenge, we propose MetaCoder, a novel meta-learning code generation approach that efficiently extracts general-purpose knowledge from a large-scale source language and rapidly adapts to domain-specific scenarios, even with relatively few samples. Method:MetaCoder employs MAML, a powerful few-shot meta-learning method, to construct a transfer learning framework. This framework learns general-purpose knowledge from large-scale source languages and applies it in domain-specific target languages. To acquire more general-purpose knowledge, heterogeneous sub-tasks are constructed from the source language during the pre-training phase of MAML. As such, combining with CodeBERT and K-means, we design an unsupervised category assignment method for code generation samples, thereby exploiting the n-way k-shot rule to construct the heterogeneous sub-tasks. Consequently, MetaCoder can be applied to the code generation field. Results:We evaluate MetaCoder with both tree-based (e.g., TreeGen) and sequence-based (e.g., CodeGPT) backbones on two domain-specific PLs, including Solidity and Bash. Extensive experiments demonstrate the superior performance of our approach compared to baselines and verified its capability of code generation visually in practice. Conclusion:MetaCoder effectively extracts general-purpose knowledge from large-scale source languages, thereby enhancing model performance. Therefore, we highly recommend MetaCoder as a code generation approach for domain-specific PLs.

The use of thematic context-based deep learning in discourse expression of sports news

Sports news is a type of discourse that is characterized by a specific vocabulary, style, and tone, and it is typically focused on conveying information about sporting events, athletes, and teams. Thematic context-based deep learning is a powerful approach that can be used to analyze and interpret various forms of natural language, including the discourse expression of sports news. An application model of sign language and lip language recognition based on deep learning is proposed to facilitate people with hearing impairment to easily obtain sports news content. First, the lip language recognition system is constructed; next, MobileNet lightweight network combined with Long-Short Term Memory (LSTM) is used to extract lip reading features. ResNet-50 residual network structure isadopted to extract the features of sign language; finally, the convergence, accuracy, precision and recall of the model are verified respectively. The results show that the loss of training set and test set converges gradually with the increase of iteration times; the lip language recognition model and the sign language recognition model basically tend to be stable after 14 iterations and 12 iterations, respectively, suggesting a better convergence effect of sign language recognition. The accuracy of sign language recognition and lip language recognition is 98.9% and 87.7%, respectively. In sign language recognition, the recognition accuracy of numbers 1, 2, 4, 6 and 8 can reach 100%. In lip language recognition, the recognition accuracy of numbers 2, 3 and 9 is relatively higher. This exploration can facilitate hearing-impaired people to quickly obtain the relevant content in sports news videos, and also provide help for their communication.