Hasil untuk "Philology. Linguistics"

Frank Rudzicz, Graeme Hirst

Luke C. Collins

Ryuko Kubota

Recent scholarship in sociolinguistics and language education has examined how race and language intersect each other and how racism influences linguistic and educational practices. While racism is often conceptualized in terms of individual and institutional injustices, a critical examination of another form of racism—epistemological racism—problematizes how racial inequalities influence our knowledge production and consumption in academe. Highlighting the importance of the intersectional nature of identity categories, this conceptual article aims to draw scholars’ attention on how epistemological racism marginalizes and erases the knowledge produced by scholars in the Global South, women scholars of color, and other minoritized groups. In today’s neoliberal culture of competition, scholars of color are compelled to become complicit with white Euro-American hegemonic knowledge, further perpetuating the hegemony of white knowledge while marginalizing women scholars of color. Valorizing non-European knowledge and collectivity as an alternative framework also risks essentialism and male hegemony. Conversely, the ethics promoted by black feminism emphasizes a personal ethical commitment to antiracism. Epistemological antiracism invites scholars to validate alternative theories, rethink our citation practices, and develop critical reflexivity and accountability.

Patrick Amadeus Irawan, Erland Hilman Fuadi, Shanu Kumar et al.

Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representation shift and cross-modal interference introduced during multimodal adaptation. Such loss is difficult to recover, even with targeted task-specific fine-tuning using standard objectives. Prior recovery approaches typically introduce additional modules that act as intermediate alignment layers to maintain or isolate modality-specific subspaces, which increases architectural complexity, adds parameters at inference time, and limits flexibility across models and settings. We propose LinguDistill, an adapter-free distillation method that restores linguistic capability by utilizing the original frozen LM as a teacher. We overcome the key challenge of enabling vision-conditioned teacher supervision by introducing layer-wise KV-cache sharing, which exposes the teacher to the student's multimodal representations without modifying the architecture of either model. We then selectively distill the teacher's strong linguistic signal on language-intensive data to recover language capability, while preserving the student's visual grounding on multimodal tasks. As a result, LinguDistill recovers $\sim$10% of the performance lost on language and knowledge benchmarks, while maintaining comparable performance on vision-heavy tasks. Our findings demonstrate that linguistic capability can be recovered without additional modules, providing an efficient and practical solution to modality-specific degradation in multimodal models.

Yuan Zhang, Thales Bertaglia

Detecting uncivil language is crucial for maintaining safe, inclusive, and democratic online spaces. Yet existing classifiers often misinterpret posts containing uncivil cues but expressing civil intents, leading to inflated estimates of harmful incivility online. We introduce LinGO, a linguistic graph optimization framework for large language models (LLMs) that leverages linguistic structures and optimization techniques to classify multi-class intents of incivility that use various direct and indirect expressions. LinGO decomposes language into multi-step linguistic components, identifies targeted steps that cause the most errors, and iteratively optimizes prompt and/or example components for targeted steps. We evaluate it using a dataset collected during the 2022 Brazilian presidential election, encompassing four forms of political incivility: Impoliteness (IMP), Hate Speech and Stereotyping (HSST), Physical Harm and Violent Political Rhetoric (PHAVPR), and Threats to Democratic Institutions and Values (THREAT). Each instance is annotated with six types of civil/uncivil intent. We benchmark LinGO using three cost-efficient LLMs: GPT-5-mini, Gemini 2.5 Flash-Lite, and Claude 3 Haiku, and four optimization techniques: TextGrad, AdalFlow, DSPy, and Retrieval-Augmented Generation (RAG). The results show that, across all models, LinGO consistently improves accuracy and weighted F1 compared with zero-shot, chain-of-thought, direct optimization, and fine-tuning baselines. RAG is the strongest optimization technique and, when paired with Gemini model, achieves the best overall performance. These findings demonstrate that incorporating multi-step linguistic components into LLM instructions and optimize targeted components can help the models explain complex semantic meanings, which can be extended to other complex semantic explanation tasks in the future.

Jian Kai, Zidong Zhang, Jiwen Chen et al.

Chinese text correction has traditionally focused on spelling and grammar, while factual error correction is usually treated separately. However, in paragraph-level Chinese professional writing, linguistic (word/grammar/punctuation) and factual errors frequently co-occur and interact, making unified correction both necessary and challenging. This paper introduces CLFEC (Chinese Linguistic & Factual Error Correction), a new task for joint linguistic and factual correction. We construct a mixed, multi-domain Chinese professional writing dataset spanning current affairs, finance, law, and medicine. We then conduct a systematic study of LLM-based correction paradigms, from prompting to retrieval-augmented generation (RAG) and agentic workflows. The analysis reveals practical challenges, including limited generalization of specialized correction models, the need for evidence grounding for factual repair, the difficulty of mixed-error paragraphs, and over-correction on clean inputs. Results further show that handling linguistic and factual Error within the same context outperform decoupled processes, and that agentic workflows can be effective with suitable backbone models. Overall, our dataset and empirical findings provide guidance for building reliable, fully automatic proofreading systems in industrial settings.

Rodney Jehu-Appiah

I propose Umwelt engineering -- the deliberate design of the linguistic cognitive environment -- as a third layer in the agent design stack, upstream of both prompt and context engineering. Two experiments test the thesis that altering the medium of reasoning alters cognition itself. In Experiment 1, three language models reason under two vocabulary constraints -- No-Have (eliminating possessive "to have") and E-Prime (eliminating "to be") -- across seven tasks (N=4,470 trials). No-Have improves ethical reasoning by 19.1 pp (p < 0.001), classification by 6.5 pp (p < 0.001), and epistemic calibration by 7.4 pp, while achieving 92.8% constraint compliance. E-Prime shows dramatic but model-dependent effects: cross-model correlations reach r = -0.75. In Experiment 2, 16 linguistically constrained agents tackle 17 debugging problems. No constrained agent outperforms the control individually, yet a 3-agent ensemble achieves 100% ground-truth coverage versus 88.2% for the control. A permutation test confirms only 8% of random 3-agent subsets achieve full coverage, and every successful subset contains the counterfactual agent. Two mechanisms emerge: cognitive restructuring and cognitive diversification. The primary limitation is the absence of an active control matching constraint prompt elaborateness.

Verena Blaschke, Masha Fedzechkina, Maartje ter Hoeve

Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families and/or a single task. It is still an open question how these findings extend to a wider variety of languages and tasks. In this work, we analyze cross-lingual transfer for 263 languages from a wide variety of language families. Moreover, we include three popular NLP tasks: POS tagging, dependency parsing, and topic classification. Our findings indicate that the effect of linguistic similarity on transfer performance depends on a range of factors: the NLP task, the (mono- or multilingual) input representations, and the definition of linguistic similarity.

Dingkang Lin, Naixuan Zhao, Dan Tian et al.

The advent of ChatGPT has profoundly reshaped scientific research practices, particularly in academic writing, where non-native English-speakers (NNES) historically face linguistic barriers. This study investigates whether ChatGPT mitigates these barriers and fosters equity by analyzing lexical complexity shifts across 2.8 million articles from OpenAlex (2020-2024). Using the Measure of Textual Lexical Diversity (MTLD) to quantify vocabulary sophistication and a difference-in-differences (DID) design to identify causal effects, we demonstrate that ChatGPT significantly enhances lexical complexity in NNES-authored abstracts, even after controlling for article-level controls, authorship patterns, and venue norms. Notably, the impact is most pronounced in preprint papers, technology- and biology-related fields and lower-tier journals. These findings provide causal evidence that ChatGPT reduces linguistic disparities and promotes equity in global academia.

Tobias Groot, Salo Lacunes, Evgenia Ilia

Natural language generation (NLG) tasks are often subject to inherent variability; e.g. predicting the next word given a context has multiple valid responses, evident when asking multiple humans to complete the task. While having language models (LMs) that are aligned pluralistically, so that they are able to reproduce well the inherent diversity in perspectives of an entire population of interest is clearly beneficial, Ilia and Aziz (2024) show that LMs do not reproduce this type of linguistic variability well. They speculate this inability might stem from the lack of consistent training of LMs with data reflecting this type of inherent variability. As such, we investigate whether training LMs on multiple plausible word continuations per context can improve their ability to reproduce human linguistic variability for next-word prediction. We employ fine-tuning techniques for pre-trained and instruction-tuned models; and demonstrate their potential when fine-tuning GPT-2 and Mistral-7B-IT, using Provo Corpus. Our evaluation, which measures divergence among empirically estimated human and model next-word distributions across contexts before and after fine-tuning, shows that our multi-label fine-tuning improves the LMs' ability to reproduce linguistic variability; both for contexts that admit higher and lower variability.

Crystal Min Hui Poon, Pai Chet Ng, Xiaoxiao Miao et al.

Instruction-guided text-to-speech (TTS) research has reached a maturity level where excellent speech generation quality is possible on demand, yet two coupled biases persist in reducing perceived quality: accent bias, where models default towards dominant phonetic patterns, and linguistic bias, a misalignment in dialect-specific lexical or cultural information. These biases are interdependent and authentic accent generation requires both accent fidelity and correctly localized text. We present CLARITY (Contextual Linguistic Adaptation and Retrieval for Inclusive TTS sYnthesis), a backbone-agnostic framework to address both biases through dual-signal optimization. Firstly, we apply contextual linguistic adaptation to localize input text to align with the target dialect. Secondly, we propose retrieval-augmented accent prompting (RAAP) to ensure accent-consistent speech prompts. We evaluate CLARITY on twelve varieties of English accent via both subjective and objective analysis. Results clearly indicate that CLARITY improves accent accuracy and fairness, ensuring higher perceptual quality output\footnote{Code and audio samples are available at https://github.com/ICT-SIT/CLARITY.

Welistony Câmara Lima, Ana Patrícia Sá Martins

Este artigo apresenta o relato de experiência de curso formativo intitulado Poder e escrita criativa com a Literatura Afro – PECLA - em parceria com o Portal Inter@ge Professor. É também fruto do estágio docente, no Programa de Pós-graduação em Letras da Universidade Estadual do Maranhão (UEMA), no âmbito da disciplina de Literatura Afro-Brasileira, ministrada para alunos do curso de Letras Licenciatura da UEMA, campus Balsas-MA. O curso teve como propósito promover formação inicial e continuada a professores e discentes sobre a escrita criativa com a Literatura Afro, a fim de aprimorarem suas habilidades literárias, pedagógicas e digitais. A metodologia adotada é qualitativa, baseada em uma narrativa autoetnográfica. Os materiais disponibilizados no ambiente virtual de aprendizagem, abordando aspectos teóricos e práticos sobre Literatura Afro e projetos didáticos, estruturam o curso em dois módulos, cada um com 30 horas. Para o embasamento teórico, utilizamos Astigarraga (2018), Passeggi (Passeggi; Souza; Vicentini, 2011) e Sanches (2022), sobre a narrativa autobiográfica; Alves (2022), sobre literaturas afro, além de Schneuwly e Dolz (2010), os quais discutem acerca dos gêneros textuais nas sequências didáticas, bem como Martins (2020), com a perspectiva teórico-formativa dos  letramentos didáticodigitais. Como resultados percebemos que, por meio das produções dos cursistas - fanfics, sequências didáticas e relatos pessoais, há interesse constante em dialogar com as recentes pesquisas sobre práticas literárias (in)visibilizadas, como as literaturas afro enquanto instrumento educacional, constituindo-se, também, como um passo importante para o letramento literário desses futuros profissionais de Letras.

Dr. Faisal Muzaffar, Dr. Muhammad Shamim, Akhter et al.

This paper presents a systematic stylistic analysis of the Qur'an, integrating the multi-level framework of Leech and Short with Classical Arabic linguistics. It examines how the text's distinctive choices across lexical, grammatical, rhetorical, and cohesive levels produce its unique expressive power. Through close analysis of selected verses, the study highlights key features including strategic ellipsis, grammatical shifts (iltifāt), and profound polysemy. These elements work in concert with intricate sound patterning to generate the Qur'an's renowned rhetorical impact (balāghah) and mnemonic quality. The analysis demonstrates that the Qur'an’s language operates as a cohesive artistic system. Key findings show that sound and meaning are deeply linked, grammar is used for deliberate rhetorical effect, and single terms often carry layered theological meanings. Furthermore, the text’s syntactic structures such as the use of nominal sentences for timeless truths contribute to its semantic depth. This linguistic artistry underpins the classical Islamic concept of inimitability (iʿjāz). By bridging traditional philology with modern stylistics, this study offers a structured approach to appreciating the Qur’an's enduring significance as a literary and linguistic phenomenon.

Cunxin Han

Corpus linguistics is a powerful quantitative methodology, which heavily relies on frequency data and statistical procedures. It is difficult to talk about corpus linguistics without mentioning sta...

Iva Gugo

This paper describes media linguistics, a linguistic discipline which studies the relationship between language and the media, i. e. which analyses how mass media professionals employ language to depict reality (Perrin 2015). The discipline was established in the late 20th and early 21st century, when it was given a name in English, German and Russian language. However, research on the language of the media had been conducted before that time. The representatives of critical linguistics (e.g. Fowler, Hodge, Tress and Krew 1979) and critical discourse analysis (e.g. van Dijk 1985, 1988) observed how language means are employed to promote political ideology in the media. Later linguists such as Bell (1991), Jucker (1992) or Fairclough (1995) shifted the focus from ideology to the analysis of lexical, syntactic and pragmatic properties of media texts. Today, media linguistics collaborates with other disciplines in conducting multidisciplinary, interdisciplinary and transdisciplinary research studies. In those studies, researchers carry out language and discourse analyses of media texts, categorize texts into genres and group genres into categories on the basis of function or some other property. They also compare texts and examine their genesis as well as the audience’s reaction to them. This paper offers several suggestions for the development of media linguistics in the Republic of Croatia, starting with the adoption of the proposed Croatian name medijska lingvistika. Among other things, it proposes the development of courses about media linguistics as a part of study programmes in the fields of philology and communication science as well a closer co–operation between linguists and communication scientists in studies about the language of the media. Further, it recommends broadening the focus of research to understudied genres and applying the methods of media linguistics to studies about the genesis of media texts in the Croatian language.

Krzysztof Siminski, Konrad Wnuk

Neuro-fuzzy systems are a technique of explainable artificial intelligence (XAI). They elaborate knowledge models as a set of fuzzy rules. Fuzzy sets are crucial components of fuzzy rules. They are used to model linguistic terms. In this paper, we present an automatic extraction of fuzzy rules in the natural English language. Full implementation is available free from a public repository.

Rosana Montes, Cristina Zuheros, Jeovani M. Morales et al.

Classic Delphi and Fuzzy Delphi methods are used to test content validity of data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solves it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution.

E. N. Orazaliyeva

The article discusses the use of the chronotopic approach in Kazakh philology. This approach integrates scholarly knowledge and the works of researchers from the last century. The purpose of the publication is to show how linguistic judgments are cyclical and how they contribute to the multidimensional understanding of language. The article aims to transform the experiences of the humanities into linguistic synchronous-diachronic combinations. This involves predicting the mutual influence of individual scientific views and their categorical integrity. The works of Kazakh researchers from the first half of the twentieth century, who underwent general linguistic training, reveal judgments about the role of language in social, ethnic, and socio-cultural realities. These judgments define a progressive understanding of language as an element of literary-ethical, moral-axiological, and spiritual-cognitive interpretations. It is advisable to consider the formation of cognitive principles of linguistic analysis in the context of spatiotemporal categories. This emphasizes the importance of cyclicity as an element of ideological evolution and scientific attitudes. The connection between past and present, preserving the progression of origin and development, becomes a source of rethinking initial judgments. The cognitive paradigm in Kazakh linguistics is chronologically systematized and reasonably synchronized with modern trends in linguistics. The article characterizes chronotopic in Kazakh linguistics as a set of historical-philosophical, literarycultural, and psychological understandings of the nature of language. The starting point of this approach is the ontological, epistemological, and linguistic dichotomy of knowledge, which determines the mechanism for constructing periodicity and rhythm in the linguistic society. The article uses comparative and historical linguistics, narrative analysis, and chronotopic reading methods. It emphasizes the need to study the spiral characteristic of the linguistics categories by focusing on the dynamics of the development of anthropocentric language features.

Sarah Lane

International Linguistics Research wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated. International Linguistics Research is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please find the application form and details at http://home.ideasspread.org/for-reviewers/ and e-mail the completed application form to ilr@ideasspread.org Reviewers for Volume 7, Number 1 Anastasia Parianou, Ionian University, Greece Bahman Solati, University of California, United States Bassil Mashaqba, The Hashemite University, Jordan Cândido Samuel Fonseca de Oliveira, Federal Center for Technological Education of Minas Gerais, Brazil Cheng-hua Hsiao, National Ilan University, Taiwan, Republic of China Elena Ogneva, Belgorod National Research University, Russia Farzaneh Shakki, Islamic Azad University, Iran Gareth Morgan, Oxford University, England Hamad H. Alsowat, Taif University, Saudi Arabia Hassan Banaru'ee, Chabahar Maritime University, Iran Hassan Moradi, Ministry of Education, Iran Irina Kuprieva, Russian University of Cooperation, Russia Laura V. Fielden Burns, University of Extremadura, English Philology Dept, Spain Mahmoud Mobaraki, University of Jahrom, Iran Mateus Cruz Maciel de Carvalho, James Cook University – Cairns, Australia Mohammad Hussein Hamdan, Al-Imam Mohammad Ibn Saud University, Saudi Arabia Mostafa Morady Moghaddam, Shahrood University of Technology, Iran Natalia Iuzefovich, Pacific National University, Russia Shadi S. Neimneh, The Hashemite University, Jordan Shinji Okumura, Mukogaw Women’s University, Japan Vera Nikolayevna Golodnaya, Nevinnomyssk Institute, Russian Federation Viktoriya N. Karpukhina, Altai State University, Russia Yasir Bdaiwi, Universiti Putra Malaysia, Iraq Yousef Awad, The University of Jordan, Jordan

Sarah Lane

International Linguistics Research wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated. International Linguistics Research is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please find the application form and details at http://home.ideasspread.org/for-reviewers/ and e-mail the completed application form to ilr@ideasspread.org Reviewers for Volume 7, Number 2 Anastasia Parianou, Ionian University, Greece Bahman Solati, University of California, United States Bassil Mashaqba, The Hashemite University, Jordan Cândido Samuel Fonseca de Oliveira, Federal Center for Technological Education of Minas Gerais, Brazil Cheng-hua Hsiao, National Ilan University, Taiwan, Republic of China Elena Ogneva, Belgorod National Research University, Russia Farzaneh Shakki, Islamic Azad University, Iran Gareth Morgan, Oxford University, England Hamad H. Alsowat, Taif University, Saudi Arabia Hassan Banaru'ee, Chabahar Maritime University, Iran Hassan Moradi, Ministry of Education, Iran Irina Kuprieva, Russian University of Cooperation, Russia Laura V. Fielden Burns, University of Extremadura, English Philology Dept, Spain Mahmoud Mobaraki, University of Jahrom, Iran Mateus Cruz Maciel de Carvalho, James Cook University – Cairns, Australia Mohammad Hussein Hamdan, Al-Imam Mohammad Ibn Saud University, Saudi Arabia Mostafa Morady Moghaddam, Shahrood University of Technology, Iran Natalia Iuzefovich, Pacific National University, Russia Shadi S. Neimneh, The Hashemite University, Jordan Shinji Okumura, Mukogaw Women’s University, Japan Vera Nikolayevna Golodnaya, Nevinnomyssk Institute, Russian Federation Viktoriya N. Karpukhina, Altai State University, Russia Yasir Bdaiwi, Universiti Putra Malaysia, Iraq Yousef Awad, The University of Jordan, Jordan