Model Of Language Research Articles

The Polyfunctionality and Origins of makee in Chinese Pidgin English

Abstract Words expressing ‘make’ are semantically and functionally versatile in many languages. Focusing on the word makee, derived from English make, in Chinese Pidgin English, a contact language originated in 18th-century Canton, China, this paper examines its origins and functions. Primarily a lexical verb, makee is polyfunctional in that it also occurs in the complex form [makee X] which indicates causation, imperative, and verbalization. Functioning as a main verb and a causative verb, makee largely mirrors the corresponding words in the source languages of the pidgin, namely English and Cantonese. However, makee as an imperative marker and a verbalizer are innovative features. In imperative sentences, makee in combination with another verb indicate commands. As a verbalizer, makee gives a verbal interpretation to the whole construction. These two new features of makee are not attributable to existing models in the source languages.

Indigenous languages as predictors of understanding and accepting COVID-19 vaccines in Nigeria and South Africa

Indigenous African languages have often suffered neglect in health development campaigns until recently. Considering the multi-faceted effects of the COVID-19 pandemic on different peoples and societies worldwide, this article argues that vaccine campaigns in African settings cannot be impactful if they are not disseminated in the indigenous African languages. The article is hinged on Salawu’s model of indigenous language for development communication, which affirms the connection between language of communication and people’s understanding of the message communicated. The study employed a survey experimental research method. Using the pool sampling technique, both online and offline questionnaires were used to survey the opinions of 191 Nigerian and 114 South African respondents in Lagos and Mafikeng, respectively, about dominant indigenous languages used for COVID-19 vaccine messages, their degree of understanding vaccine messages and their acceptance or rejection of the vaccine messages. Interview sessions were conducted with five purposively selected health communication and media experts to determine the significance of and challenges encountered in the use of indigenous African languages for COVID-19 vaccine campaigns. The quantitative data were analysed using descriptive and inferential statistical tools, such as frequency count and percentage, chi-square, percentile and logistic regression; while the qualitative data were thematically analysed. While respondents mostly identified with messages in Yoruba, Igbo, Setswana and isiZulu, a few respondents identified with a combination of languages, although these languages did not influence their acceptance of vaccines.

Tokenization Stability Index: A Catalyst for Optimizing Transformer Models for Low Resource Languages

Tokenization Stability Index: A Catalyst for Optimizing Transformer Models for Low Resource Languages

Beauty Contest in Equity-Based Crowdfunding Campaigns

Purpose This study investigates how investor sentiment influences equity-based crowdfunding campaigns outcomes, with a particular focus on sentiment related to specific industries. The motivation stems from the need to understand the behavioural drivers behind crowdfunding outcomes, especially in a context where both private and public funding coexist. Study Design We employed a machine learning approach, utilizing the PolBERT model to analyse investor sentiment from online forum discussions in Poland. The data consisted of sentiment expressed across various industry-specific discussions, which were analysed to assess their impact on crowdfunding campaign outcomes. Findings The study reveals that while positive investor industry sentiment enhances the capital raised for IPO-targeted companies, it negatively affects overall campaign success and the number of investors, particularly in non-IPO-targeted firms. These findings suggest a complex relationship between sentiment and campaign outcomes, where higher sentiment can sometimes deter participation in certain cases. Contributions This research introduces a novel category of behavioural success drivers in the context of equity crowdfunding, proposing a new classification of meta-drivers of crowdfunding success. Methodologically, the study demonstrates the efficacy of fine-tuned BERT-class models in processing language, offering new insights into the investor sentiment analysis. Implications The results have significant implications for both researchers and practitioners. For researchers, the study expands the understanding of behavioural finance in crowdfunding, while for practitioners, it provides strategic insights into how investor sentiment can influence campaign timing, structure, and expectations. The findings also offer value for policymakers by shedding light on investor behaviour in equity-based crowdfunding markets.

Subword Dictionary Learning and Segmentation for Expanding the Vocabulary of Automatic Speech Recognition in Tamil and Kannada

We present automatic speech recognition (ASR) systems for Tamil and Kannada based on subword modeling to effectively handle unlimited vocabulary arising due to the highly agglutinative nature of the languages. We propose a variant of the byte pair encoding (BPE) algorithm named extended-BPE, and Morfessor tool to segment each word into its subwords. We have effectively incorporated maximum likelihood and Viterbi estimation techniques with weighted finite state transducers framework in these algorithms to learn the subword dictionary from a large text corpus. Using the learned subword dictionary, the words in the transcriptions of the training data are segmented into subwords. We train deep neural network ASR systems that recognize the subword sequence for any given test speech utterance. The output subword sequence is then post-processed using deterministic rules to get the final word sequence. Because of this subword design, the number of words that can be recognized is much larger than the number of words in the training corpus. For Tamil ASR, we use 152 hours of data for training and 65 hours for testing, whereas for Kannada ASR, we use 275 hours for training and 72 hours for testing. Upon experimenting with different combinations of segmentation and estimation techniques, we find that the word error rate (WER) reduces drastically when compared to the baseline word-level ASR, achieving a maximum absolute WER reduction of 6.24% and 6.63% for Tamil and Kannada, respectively. Further, comparing our results with that of an end-to-end ASR model available on Github, we have seen that our subword language models can perform comparable to or exceed that of recent end-to-end ASR models for Kannada and Tamil languages.

A model of language and methodological pre-service teachers’ training based on artificial intelligence technologies

Importance. Existing empirical research in the field of integration of artificial intelligence technology in foreign language teaching is devoted to the use of specific technology in teaching types of speech activity, mainly writing. The authors note the wide methodological potential of artificial intelligence technologies in foreign language teaching and use chatbots, voice assistants, intelligent learning systems, corpus technologies to form the foreign language communicative competence of students. However, the analysis of a number of studies has allowed us to conclude that so far the authors have not attempted to design a unified model of language and methodological preservice teachers’ training based on artificial intelligence technologies. The purpose of this work is to design a model of language and methodological pre-service teachers’ training based on artificial intelligence technologies.Research Methods. The present study is related to the study of the context of the integration of artificial intelligence technologies into language education. To achieve the set research goal, theoretical methods were used: the study and analysis of scientific and methodological works on thedesign of methodological models of teaching a foreign language using modern technologies; empirical methods: survey, observation and description of research results; modeling methods.Definition of Concepts. The main concepts in this work are “the model of language and methodological pre-service teachers’ training” and “the competence of a pre-service foreign language teacher in the field of using artificial intelligence technologies”.Results and Discussion. Structurally, the model of language and methodological pre-service teachers’ training based on artificial intelligence technologies is represented by the following components: prerequisites (determining the relevance of designing a learning model), a goalsetting block (setting goals and objectives for developing teaching methods), a theoretical block (determining the theoretical and methodological basis of research), a technological block (determining strategies and teaching methods, selection of the learning content, identification of organizational and pedagogical learning conditions, the choice of optimal organizational forms of learning, the definition of pedagogical tools), the evaluation and performance block (the development of a criterion-based assessment apparatus and the forecast of expected learning outcomes).Conclusion. At this stage, the proposed model reflects the essence of the development of AI technologies and their applicability in a foreign language teaching. The separation of artificial intelligence from a means of learning into a separate subject of the educational process indicates that there is a paradigm shift in the use of new technologies in learning. AI technologies are able to provide high-quality feedback, create additional conditions for language practice, take on daily routine tasks and automate them, thereby shaping the ability of students to engage in their education and self-education throughout their lives. The obtained research results are recommended to be used in the methodology of teaching a foreign language, as well as in the development of private methods of teaching a foreign language using AI technologies.

The Antecedents of Transformer Models

Transformer models of language represent a step change in our ability to account for cognitive phenomena. Although the specific architecture that has garnered recent interest is quite young, many of its components have antecedents in the cognitive science literature. In this article, we start by providing an introduction to large language models aimed at a general psychological audience. We then highlight some of the antecedents, including the importance of scale, instance-based memory models, paradigmatic association and systematicity, positional encodings of serial order, and the learning of control processes. This article offers an exploration of the relationship between transformer models and their precursors, showing how they can be understood as a next phase in our understanding of cognitive processes.

A Study on Bilingual Deep Learning PIS Neural Network Model Based on Graph-Text Modal Fusion

This research investigates a multilingual cross-modal pedestrian information search (PIS) technique based on graph-text modal fusion. Initially, we used a combination of replacement neural networks to improve the English Language-Based Pedestrian Information Search model with Graph-Text Modal Fusion (GTMFLPIS) performance. In addition, existing research lacks GTMFLPIS models for other languages. Therefore, we propose to train GTMFLPIS models for Chinese. The Chinese GTMFLPIS model was trained using our previously constructed Chinese CUHK-PEDES dataset. The Rank1 of the Chinese RN50_PMML12V2 model reached 0.5989. In addition, we found that a single model could not adapt to the limitations of multiple languages. Therefore, we propose a novel architecture to implement a single-model multilingual cross-modal GTMFLPIS model in this research. We propose RN50_DBMCV2 and ENB7_DBM-CV2, both of which have improved performance over the existing ones. We constructed a bilingual dataset using our Chinese CUHK-PEDES dataset and existing English CUHK-PEDES dataset to test our novel multilingual cross-modal GTMFLPIS model. In addition, we found that the loss function significantly impacts the model during our experiments. Therefore, we optimized the performance of the existing loss functions for cross-modal GTMFLPIS models. Our proposed CCMPM loss function improves the performance of the model by 2%. The experimental results of this research show that our proposed model has advantages in improving the accuracy of PIS.

Barriers and Facilitators to Patient Utilization of Non-Communicable Disease Services in Primary Healthcare Facilities in Nepal: A Qualitative Study.

Background The Nepalese government endorsed and implemented the Package of Essential Non-Communicable Disease Interventions (PEN) by the World Health Organization (WHO) to prevent and manage four major non-communicable diseases (NCDs): cardiovascular disease (CVD), diabetes, cancers, and chronic respiratory diseases. This study explored barriers and facilitators to patient utilization of NCD services at primary healthcare facilities in Nepal. Methodology: We conducted a qualitative study with a 35 purposive sample of patients living with one or more NCDs (hypertension, diabetes, chronic obstructive pulmonary disease (COPD/ asthma) who sought healthcare at primary healthcare facilities in 14 randomly selected districts in seven provinces in Nepal that implemented PEN. Trained qualitative researchers conducted in-depth interviews in-person in a private setting using a semi-structured interview guide developed based on the Health Belief Model in the local language. The interviews were audio-recorded, transcribed verbatim, coded inductively and deductively, and analyzed by a framework approach using Dedoose software. Results From the perspectives of patients, key facilitators of service utilization encompassed free medicines, low-cost services, geographical and financial accessibility, less waiting time, positive interactions with health service providers, experiencing improvements in their health conditions, and support from family and peers. Barriers to utilizing services included inadequate health services (e.g., lack of medications and equipment), inaccessibility and affordability, inadequate health-related information from health service providers, low knowledge of NCD care, and lack of reminders or follow ups. Conclusion Enhancing NCD service utilization is potentially attainable through interventions that address patients' knowledge, self-motivation, and misconceptions. Furthermore, strengthening the availability and accessibility of crucial services such as laboratory investigations, medications, equipment, and the patient-provider relationship is crucial for sustainable implementation of PEN.

Fake News Detection in Hausa Language Using Transfer Learning Method

Fake news poses a significant threat to societies worldwide, including in Hausa-speaking regions, where misinformation is rapidly disseminated via social media. The lack of NLP resources tailored to this language exacerbated the problem of fake news in the Hausa language. While extensive research has been conducted on counterfeit news detection in languages such as English, little attention has been paid to languages like Hausa, leaving a significant portion of the global population vulnerable to misinformation. Traditional machine-learning approaches often fail to perform well in low-resource settings due to insufficient training data and linguistic resources. This study aims to develop a robust model for detecting fake news in the Hausa language by leveraging transfer learning techniques with adaptive fine-tuning. A dataset of over 6,600 news articles, including both fake and truthful articles, was collected from various sources between January 2022 and December 2023. Cross-lingual transfer Learning (XLT) was employed to adapt pre- trained models for the low-resource Hausa language. The model was fine-tuned and evaluated using performance metrics such as accuracy, precision, recall, F-score, AUC-ROC, and PR curves. Results demonstrated a high accuracy rate in identifying fake news, with significant improvements in detecting misinformation within political and world news categories. This study addresses the gap in Hausa- language natural language processing (NLP) and contributes to the fight against misinformation in Nigeria. The findings are relevant for developing AI- driven tools to curb fake news dissemination in African languages.

Metapragmatic conventions and integrative context: Introducing Sībawayhian pragmatics

Abstract The aim of this paper is to shed light on theories of discourse meaning from the Arabic, namely Sībawayhian, linguistics tradition, that are novel in the contemporary pragmatics space and prompts us to rethink and reshape the Anglo-American, namely Gricean and post-Gricean, lens through which current theories are predominantly defined. The Arabic linguistics tradition was founded by Sībawayhi more than ten centuries ago, yet it is severely underrepresented and under-researched in modern (Western) linguistics. Sībawayhi posited a discourse-oriented, pragmatics-centric, and intuition-driven model of language and communication, providing unique insights into how intentions and conventions figure in meaning representation. Despite his monumental contribution, Sībawayhian theory has hitherto been missing from the present-day field of pragmatics. Instead, pragmatics as a line of study is usually attributed to Grice and subsequent post-Gricean developments, where theory construction has been limited to intra-theoretical analyses and falls short of cross-cultural epistemological perspectives. Many of these analyses are as such reinventions of what can be found in Sībawayhian and post-Sībawayhian pragmatics. And there are many more components of Arabic pragmatic thought that remain to be (re)discovered, with the potential to further current thinking in pragmatics and open a new orientation of contemporary pragmatic study. In this paper, I attempt to address all of the above by closely examining selected sections of Sībawayhi’s monumental al-Kitāb. I particularly introduce two main conceptions Sībawayhi relies on in his theory of meaning – metapragmatic conventions (conventionsmp) and integrative context (contexti) – and investigate their role in shaping major areas of pragmatic theorization, namely syntax, discourse compositionality, and inference.

Электронный корпус татарского языка на базе модели лингвистических графов знаний

The article presents a new version of the electronic corpus of the Tatar language, updated based on a linguistic knowledge graph model for Turkic languages. This new version of the corpus allows for information description across multiple linguistic levels: morphonological, syntactic, and semantic, through the use of knowledge graphs to represent linguistic data. This approach enhances corpus functionality, enabling searches that incorporate syntactic and semantic information. A distinctive feature of the electronic corpus implementation is that the model employed aligns closely with the structural and functional characteristics of Turkic languages and serves as a foundation for developing various software products for semantic text processing in Turkic languages. In particular, these products include the linguistic portal "Turkic Morphme" and the new version of the Tatar language electronic corpus, "Tugan Tel.".

Linguistic models of abusive language

ABSTRACT Abusive language and hate speech that incites violence is a growing problem for governments trying to manage social media posts on the internet. Such language incites civil disorder by angering target individuals and groups, and tends to reinforce the identity and cohesiveness of those who use it. Abusive language is an intelligence target because it can be a leading indicator of violence. Black box predictors can achieve high accuracy but they reveal nothing about the structures, both social and technical, that underlie abusive language. We build a set of abusive language predictors leveraging both social constructs such as otherness, and linguistic properties. A stacking predictor then determines the most significant component predictors. We achieve prediction accuracy of 96.5%, with an 88% accuracy for abusive documents. This is an improvement of more than 16% points in detecting abusive documents compared to a popular empirically derived predictors, and provides insights into the mechanics of abusive language.

Incoherent coherence? Using systemic functional linguistics to improve oral language assessment literacy

AbstractThis paper discusses the challenges of defining coherence in the context of oral language assessment literacy and proposes that better understanding of the construct can be achieved through a systemic-functional linguistic lens. Coherence is taken to be a foundational quality of written and spoken discourse and is a standard feature in the assessment rubrics of most large-scale English-language oral proficiency tests such as IELTS, TOEFL, and Cambridge. However, the precise meaning of coherence, and how it can be identified in language, is difficult to grasp. This has implications for the makers and raters of tests as well as the candidates who take them. The literature suggests coherence is the most troublesome category to rate reliably and validly. The systemic-functional linguistic (SFL) model of language provides a rich description of coherence, but has been under-utilised by the language testing industry to date. In this paper we examine three aspects of coherence: (1) how it is currently conceptualised in oral language proficiency testing; (2) how it is conceptualised in linguistics in general and SFL in particular; and (3) how insights from SFL could inform the evaluation of coherence in oral language proficiency examinations.

The Politics of Memory: NLP Models as Liberating Artifacts

The main argument of this article is that natural language processing (NLP) projects of Indigenous African languages can be understood as an effort to restore and preserve Africa’s memory in the digital. Language has always occupied a particular place in the African decolonial imaginary. Several African states embarked on processes of Africanisation of postcolonial institutions in their respective national contexts. One of the main features of this project in many African countries was the restoration of African languages in education and public service. Taking a social constructivist approach rooted in science and technology studies and looking through the analytical lens of decoloniality in Africa, I conceptualise NLP models of Indigenous African languages as liberating artifacts. I mobilise decolonial theory to look at language as a technology that can be used to dis-member and re-member the African subjects of their memory of being as people. The coloniser erased the memory of the African subjugated by cutting off their linguistic connections and instead planted the European memory. From this perspective, decolonisation can be understood as a process of re-memberment that can be achieved through the return of the languages of those whose humanity has been denied or questioned. In this sense, NLP projects offer instances of the digital that represent spaces of restoration and negotiated digital futurities. I contend that these digital spaces in Africa are social spaces of contestation about what common investments should be made, what collective futures are desirable and what are the perceived material consequences of such shared imaginations.

The four strands: A simplified approach to SLA for CLIL

AbstractWhile it is widely accepted that language teachers require knowledge of second language acquisition (SLA) (Richards, 2010), what about content teachers, who are increasingly required to help develop their learners' academic language proficiency? How much do they need to know about SLA theories and how can they acquire such knowledge and expertise? This article proposes P. Nation's (2007) four strands framework as a straightforward and accessible model for content and language integrated learning (CLIL) teachers to address the challenge of balancing content and language instruction, a common concern within CLIL programs. The article begins by looking at two well‐known CLIL teacher competence frameworks. It then raises questions about the extent to which CLIL teachers need to master aspects of pedagogical content knowledge about SLA. The authors argue that while CLIL teachers do indeed need knowledge of how languages are learned and of SLA‐informed approaches to developing learners' second language (L2) proficiency, questions about just how much SLA knowledge CLIL teachers need and how they can be equipped with this knowledge remain unanswered. By delineating language learning opportunities into the four strands framework—meaning‐focused input, meaning‐focused output, language‐focused learning, and fluency development—the authors illustrate how this framework offers a practical guide for integrating language and content education effectively in CLIL and bilingual education contexts. The article ends with a presentation of three principles to help CLIL teachers achieve both the content and language teaching objectives of CLIL.

Introducing an Epistemological Paradigm and Its Ontological Origin through the Metaphysical Deconstruction of Language as the Model of Expression

The model of language can relatively concretely reveal the mechanism of the epistemology, which is extended by the ontology that takes “person” as the unit. It unveils an intuitable dimension to represent the epistemolog’s paradigm and limitations. Deriving from the inherent relation between epistemology and ontology, the retrospection of the epistemology’s ontological origin can be actualized by locating or grasping the epistemological first person’s ontological essence. Hence, the illusional and frail essence of the ontology behind this epistemology would be uncovered. “Person” is not a stable unit in the ontological world, since the application of this unit stimulates the development of the operating style of the epistemology which relies on the supplement of paradox. After this study that intends to get the epistemological and ontological truth of the current using nominative, the new era of one or more unknown but solid epistemology and its ontology will be gradually recognized. Furthermore, the circumstance of past and current philosophical study is going to be changed.

Exploring Meditron-7b performance on a small-scale purpose made dataset in human nutrition queries

Abstract The interest in Large Languages Models (LLMs) has been rapidly evolving over the last years, boosting concerns about their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. In this study, we aimed to test the performance of MEDITRON, a recently released suite of open-source LLMs with 7B parameters adapted to the medical domain, on a small sample of human nutrition queries. To facilitate our assessment, we curated a specialized dataset comprising a diverse range of human nutrition-related queries, which have been manually collected from various medical databases and nutrition exams. The dataset was adapted for the scope of our analysis to encompass variations in language complexity, query types, and semantic nuances commonly encountered in real-world settings. Additionally, to ensure a standardized and clinically relevant context for our evaluations, we engineered a specialized prompt designed to mimic interactions with a highly esteemed physician specializing in nutrition, food science, and diet-related disorders. The prompt guided the generation of nutrition-related queries in a structured format, enabling MEDITRON to provide responses consistent with the latest advancements in medical research. Our preliminary findings revealed promising capabilities of MEDITRON in understanding medical language, providing contextually appropriate responses to several human nutrition related questions. Through this study, we add valuable insights to the ongoing discussion around the deployment of LLMs in public health, highlighting their potential to improve access to essential human nutrition literacy. Key messages • Design of LLMs tools holds the potential to improve healthcare in clinical, research and education applications. • Further evidence is needed to foster their actual integration in real world settings.

Идиостиль автора: вопросы параметризации

The paper presents a systematization of the parameters identified for the study of the author’s individual style, which are found in scientific works of recent years, with the aim of creating a general research model. The work presents the results of analysing dissertations from 1996 to 2024, in which authors address the analysis of the author’s individual style in terms of material, object, or subject of study. As a basis for further systematization, features such as research approach, material, understanding of individual style, and, finally, the analyzed parameters were adopted. This work is the first one to present a systematization of parameters included in the system of the author’s individual style, based on dissertation research conducted within different fields, using different approaches and diverse material, with the aim of creating a general model, which accounts for the scientific novelty. The research findings are as follows: a system of parameters has been identified and subjected to authorial classification, consisting of six levels, which includes elements of a level model of language, a pragmatic component (functionality), and the author’s worldview.

Efficient labeling of french mammogram reports with MammoBERT

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.