Hasil untuk "Language. Linguistic theory. Comparative grammar"

W. C. Ritchie, T. Bhatia

W.C. Ritchie and T.K. Bhatia, Second Language Acquisition: Introduction, Foundations, and Overview. Research and Theoretical Issues in Second Language Acquisition: K.R. Gregg, The Logical and Developmental Problems of Second Language Acquisition. Issues of Maturation and Modularity in Second Language Acquisition: L. White, Universal Grammar and Second Language Acquisition: Current Trends and New Directions. S. Flynn, A Parameter-Setting Approach to Second Language Acquisition. J. Schachter, Maturation and the Issue of Universal Grammar in Second Language Acquisition. F.R. Eckman, A Functional-Typological Approach to Second Language Acquisition Theory. B. McLaughlin and R. Heredia, Information-Processing Approaches to Research on Second Language Acquisition and Use. D. Preston, Variationist Linguistics and Second Language Acquisition. Second Language Speech and the Influence of the First Language: J. Leather and A. James, Second Language Speech. S. Gass, Second Language Acquisition and Linguistic Theory: The Role of Language Transfer. Research Methodology and Applications: D. Nunan, Issues in Second Language Acquisition Research: Examining Substance and Procedure. A. Sorace, The Use of Acceptability Judgments in Second Language Acquisition Research. Modality and the Linguistic Environment in Second Language Acquisition: M.H. Long, The Role of the Linguistic Environment in Second Language Acquisition. G.P. Berent, The Acquisition of English Syntax by Deaf Learners. The Neuropsychology of Second Language Acquisition and Use: L.K. Obler and S. Hannigan, Neurolinguistics of Second Language Acquisition and Use. Language Contact and its Consequences: R.W. Anderson and Y. Shirai, The Primacy of Aspect in First and Second Language Acquisition: The Pidgin-Creole Connection. S. Romaine, Bilingualism. H.W. Seliger, Primary Language Attrition in the Context of Bilingualism. T.K. Bhatia and W.C. Ritchie, Bilingual Language Mixing, Universal Grammar, and Second Language Acquisition. Glossary. Author Index. Subject Index.

Robert W. Bley-Vroman

R. Ellis

M. Halle, J. Bresnan, G. Miller

Xinyun Huo, Raghav Gnanasambandam, Xinyao Zhang

Human-robot collaboration in industrial settings requires precise and reliable communication to enhance operational efficiency. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable industrial commands. To address this gap, this paper introduces a novel grammar-constrained LLM that integrates a grammar-driven Natural Language Understanding (NLU) system with a fine-tuned LLM, which enables both conversational flexibility and the deterministic precision required in robotics. Our method employs a two-stage process. First, a fine-tuned LLM performs high-level contextual reasoning and parameter inference on natural language inputs. Second, a Structured Language Model (SLM) and a grammar-based canonicalizer constrain the LLM's output, forcing it into a standardized symbolic format composed of valid action frames and command elements. This process guarantees that generated commands are valid and structured in a robot-readable JSON format. A key feature of the proposed model is a validation and feedback loop. A grammar parser validates the output against a predefined list of executable robotic actions. If a command is invalid, the system automatically generates corrective prompts and re-engages the LLM. This iterative self-correction mechanism allows the model to recover from initial interpretation errors to improve system robustness. We evaluate our grammar-constrained hybrid model against two baselines: a fine-tuned API-based LLM and a standalone grammar-driven NLU model. Using the Human Robot Interaction Corpus (HuRIC) dataset, we demonstrate that the hybrid approach achieves superior command validity, which promotes safer and more effective industrial human-robot collaboration.

Jack Ng Kok Wah

Artificial Intelligence (AI) is transforming language learning by offering personalized, adaptive, and emotionally responsive educational experiences. This review synthesizes findings from 26 recent empirical and theoretical studies to evaluate the effectiveness of AI tools such as chatbots, pedagogical agents, and generative AI in enhancing learner engagement, reducing foreign language anxiety, and improving vocabulary acquisition. The results indicate that AI-driven systems contribute to better vocabulary retention, emotional regulation, and learner motivation, particularly when informed by educational theories like self-determination and design thinking. Despite these benefits, the review identifies significant challenges, including digital inequality, insufficient teacher training, algorithmic bias, and a limited linguistic range. While AI can promote learner autonomy and provide low anxiety learning environments, it may also lead to technostress and dependency if not properly integrated with pedagogical support. The study highlights the importance of educator preparedness and ethical AI implementation. Using qualitative-comparative and bibliometric analysis, the review proposes a multidimensional model that emphasizes adaptive feedback, emotional scaffolding, and theoretical alignment. It calls for inclusive AI design, equitable access to technology, and continuous professional development for educators. Future research should adopt longitudinal, interdisciplinary, and culturally adaptive frameworks to examine AI's long-term and sustainable impact on language acquisition in varied educational settings.

Ghulam Abbas Ahmar, Muhammad Wasif, Imran Ejaz

This study examines the quality of two English translations of Bulleh Shah’s famous Punjabi poem “Ilmoun Bas Kari O Yaar.” One translation is by Kartar Singh Duggal and the other is by R. A. Nicholson. The research employs Eugene Nida’s model of equivalent effect to assess how effectively each translation conveys the poem’s profound spiritual meaning and intricate cultural nuances. This research presents a significant challenge in translating Sufi poetry into various languages. Using a qualitative comparative analysis, the study evaluates both translations in light of Nida’s concepts of dynamic and formal equivalence. It measures how well they maintain semantic accuracy, emotional depth, and stylistic integrity. The findings show that Duggal’s translation captures the mystical intensity and lyrical quality of the original poem. In contrast, Nicholson’s more rigid style sometimes loses meaning and emotional resonance. The analysis highlights the challenges of translating mystical poetry, where cultural context and spiritual themes are equally important as word choice. By highlighting these differences in translation, the study contributes to the broader discussion about interpreting texts across languages and cultures. It sheds light on the balance between faithfulness to the original form and conveying the essence of the poetry. Ultimately, this research demonstrates that dynamic equivalence, as exemplified in Duggal’s translation, is more effective in preserving the transcendent quality of Bulleh Shah’s poetry. References Abdelaal, N. M., & Md Rashid, S. (2016). Grammar-related semantic losses in the translation of the Holy Quran, with special reference to Surah Al A’araf (The Heights). SAGE Open, 6(3), 1–10. https://doi.org/10.1177/2158244016661750 Al-Ghazalli, M. F. (2012). A study of the English translations of the Qur’anic verb phrase: The derivatives of the triliteral. Theory and Practice in Language Studies, 2(3), 605–612. https://doi.org/10.4304/tpls.2.3.605-612 Chopra, R. M. (2006). The wisdom of Bullah Shah. Bharatiya Vidya Bhavan. Deol, M. S. (2000). Bulleh Shah: The poet of love and light. Harman Publishing House. Hassan, M. (1998). The Sufi poetry of Bulleh Shah. Routledge. Jabak, O. O. (2020). Application of Eugene Nida’s theory of translation to the English translation of Surah Ash-Shams. TranscUlturAl: A Journal of Translation and Cultural Studies, 12(2), 3–18. https://doi.org/10.21992/tc29461 Jawaid, A., Batool, M., Arshad, W., Haq, M. I. U., Kaur, P., & Arshad, S. (2025). English language vocabulary building trends in students of higher education institutions and a case of Lahore, Pakistan. Contemporary Journal of Social Science Review, 3(1), 730–737. https://contemporaryjournal.com/index.php/14/article/view/360 Jawaid, A., Batool, M., Arshad, W., Kaur, P., & Haq, M. I. U. (2024). English language pronunciation challenges faced by tertiary students. Contemporary Journal of Social Science Review, 2(4), 2104–2111. https://contemporaryjournal.com/index.php/14/article/view/361 Ketkar, S. (2021). Literary translation: Recent theoretical developments. Translation Directory. Retrieved from https://www.translationdirectory.com Lefevere, A. (1984). That structure in the dialect of men interpreted. Comparative Criticism, 6, 87–100. Miao, J. (2000). The limitations of ‘equivalent effect’. Perspectives, 8(3), 197–205. https://doi.org/10.1080/0907676X.2000.9961388 Nida, E. A. (1964). Toward a science of translation: With special reference to principles and procedures involved in Bible translating. Leiden: E. J. Brill. Nida, E. A., & Taber, C. R. (1969). The theory and practice of translation. Leiden: E. J. Brill. Panou, D. (2013). Equivalence in translation theories: A critical evaluation. Theory and Practice in Language Studies, 3(1), 1–6. Pritchard, W. H. (1960). Diminished nature. The Massachusetts Review, 1(3), 475–492. Roman Jakobson. (1969). On the linguistic aspects of translation. In S. Gabrielyan (Ed.), Translation studies reader (pp. 202–215). Yerevan: Sahak Partev. Schimmel, A. (1963). Gabriel’s wing: A study into the religious ideas of Sir Muhammad Iqbal (Vol. 6). Brill Archive. Schimmel, A. (1975). Mystical dimensions of Islam. University of North Carolina Press. Shakernia, S. (2013). Study of Nida’s (formal and dynamic equivalence) and Newmark’s (semantic and communicative translation) translating theories on two short stories. Merit Research Journal of Education and Review, 2(1), 1–7. Venuti, L. (2011). World literature and translation studies. In The Routledge companion to world literature (pp. 202–215). Routledge. Wang, D. (2017, February). Functionalist translation theory and its application in literary translation. In 2016 2nd International Conference on Education, Social Science, Management and Sports (ICESSMS 2016) (pp. 330–334). Atlantis Press.

Laura Ying Schulz, Daniel Mitropolsky, Tomaso Poggio

While large models achieve impressive results, their learning dynamics are far from understood. Many domains of interest, such as natural language syntax, coding languages, arithmetic problems, are captured by context-free grammars (CFGs). In this work, we extend prior work on neural language modeling of CFGs in a novel direction: how language modeling behaves with respect to CFG substructure, namely "subgrammars". We first define subgrammars, and prove a set of fundamental theorems regarding language modeling and subgrammars. We show that language modeling loss (or equivalently the Kullback-Leibler divergence) recurses linearly over its top-level subgrammars; applied recursively, the loss decomposes into losses for "irreducible" subgrammars. We also prove that the constant in this linear recurrence is a function of the expected recursion, a notion we introduce. We show that under additional assumptions, parametrized models learn subgrammars in parallel. Empirically, we confirm that small transformers learn subgrammars in parallel, unlike children, who first master simple substructures. We also briefly explore several other questions regarding subgrammars. We find that subgrammar pretraining can improve final performance, but only for tiny models relative to the grammar, while alignment analyses show that pretraining consistently lead to internal representations that better reflect the grammar's substructure in all cases; we also observe persistent difficulty with deeper recursion, a limitation that appears even of large language models.

Nevidu Jayatilleke, Nisansa de Silva

Solving the problem of Optical Character Recognition (OCR) on printed text for Latin and its derivative scripts can now be considered settled due to the volumes of research done on English and other High-Resourced Languages (HRL). However, for Low-Resourced Languages (LRL) that use unique scripts, it remains an open problem. This study presents a comparative analysis of the zero-shot performance of six distinct OCR engines on two LRLs: Sinhala and Tamil. The selected engines include both commercial and open-source systems, aiming to evaluate the strengths of each category. The Cloud Vision API, Surya, Document AI, and Tesseract were evaluated for both Sinhala and Tamil, while Subasa OCR and EasyOCR were examined for only one language due to their limitations. The performance of these systems was rigorously analysed using five measurement techniques to assess accuracy at both the character and word levels. According to the findings, Surya delivered the best performance for Sinhala across all metrics, with a WER of 2.61%. Conversely, Document AI excelled across all metrics for Tamil, highlighted by a very low CER of 0.78%. In addition to the above analysis, we also introduce a novel synthetic Tamil OCR benchmarking dataset.

Terra Blevins, Susanne Schmalwieser, Benjamin Roth

While large language models (LLMs) are generally considered proficient in generating language, how similar their language usage is to that of humans remains understudied. In this paper, we test whether models exhibit linguistic convergence, a core pragmatic element of human language communication: do models adapt, or converge, to the linguistic patterns of their user? To answer this, we systematically compare model completions of existing dialogues to original human responses across sixteen language models, three dialogue corpora, and various stylometric features. We find that models strongly converge to the conversation's style, often significantly overfitting relative to the human baseline. While convergence patterns are often feature-specific, we observe consistent shifts in convergence across modeling settings, with instruction-tuned and larger models converging less than their pretrained and smaller counterparts. Given the differences in human and model convergence patterns, we hypothesize that the underlying mechanisms driving these behaviors are very different.

Ikramjon Abdullayev

This paper presents a unified structural theory of parts of speech, integrating comparative evidence from English, Uzbek, and Russian. It addresses the longstanding debate on word-class categorization by combining distributional, syntactic, morphological, and semantic criteria into a comprehensive framework. Utilizing descriptive grammars and typological studies, we identify universally communicative "primary" parts of speech common across these languages, alongside "secondary" categories specific to individual linguistic structures. Our analysis emphasizes the importance of clearly distinguishing semantic and formal criteria to avoid theoretical ambiguity. The unified model effectively accounts for cross-linguistic variations, such as differing usage of articles, pronoun case systems, and morphological distinctions, ensuring theoretical coherence.

Suleiman Ibrahim Shelash Mohammad, N. Yogeesh, K. Al-Daoud et al.

This study investigates the integration of fuzzy logic into intelligent language tutoring systems to address the inherent uncertainties in language learning. By employing continuous membership functions, fuzzy inference mechanisms, and defuzzification techniques, the proposed system adapts instructional content and provides personalized feedback in real time. An experimental case study involving 25 intermediate-level language learners over a 16-week academic semester was conducted. Baseline assessments measured initial proficiency, followed by a tutoring intervention where fuzzy logic dynamically adjusted content based on learner performance, and concluding with post-intervention evaluations. Quantitative analysis showed an overall increase of 12.24 points on the pre-test and post-test while qualitative feedback highlighted more engagement, confidence as learner and satisfaction from the adaptive feedback technique. The fuzzy logic system proved to be significantly more effective in managing linguistic vague phenomena (like pronunciation, grammar, etc.) than with the control group (comparative with traditional tutoring). These results not only demonstrate the mathematical strength of fuzzy logic in education, but also suggest its use in improving individualized language learning. Future research will examine sustainable impacts, synergies with other Al technologies, and approaches to scaling the system to different educational settings. In addition, the study also includes rigorous mathematical modelling and sensitivity analysis to demonstrate the stability of fuzzy membership functions and the inference mechanism. A rigorous statistical significance test rigorously affirms the significant effectiveness of the system, validating its merit as a trusted device for customized language education.

Martina Wiltschko

This paper explores the relation between language and emotion and thus contributes to both language sciences and affective sciences. In both fields, insights from the other field are conspicuously absent. The core empirical claim presented is that there are no grammatical categories dedicated to encoding emotions. This seems to be universally the case and hence appears to be no accident. The absence of grammatical categories dedicated to encoding emotions is surprising given the otherwise close connection between language and emotions as evidenced by phylogenetic, ontogenetic, and neurological properties. Hence, one cannot attribute the absence of emotion categories to a complete disconnect between language and emotions (or cognition more generally). Moreover, one might expect such categories to exist, based on cognitive and evolutionary considerations. The conclusion to be drawn is that emotions are not to be considered primitives that could be directly linked to grammatical categories, but instead that emotions are constructed. In this way, the properties of grammar provide new evidence for the theory of constructed emotions. It is further proposed that linguistic theory may shed light on how emotions are constructed. Specifically, the article explores the hypothesis that the same architecture is responsible for the construction of complex linguistic expressions and for the construction of emotions. As such, the article introduces a novel research agenda, i.e. the emotional spine hypothesis, which invites new avenues of interdisciplinary research.

Jonathan Crawford

Parkinsons disease is the second most prevalent neurodegenerative disorder with over ten million active cases worldwide and one million new diagnoses per year. Detecting and subsequently diagnosing the disease is challenging because of symptom heterogeneity with respect to complexity, as well as the type and timing of phenotypic manifestations. Typically, language impairment can present in the prodromal phase and precede motor symptoms suggesting that a linguistic-based approach could serve as a diagnostic method for incipient Parkinsons disease. Additionally, improved linguistic models may enhance other approaches through ensemble techniques. The field of large language models is advancing rapidly, presenting the opportunity to explore the use of these new models for detecting Parkinsons disease and to improve on current linguistic approaches with high-dimensional representations of linguistics. We evaluate the application of state-of-the-art large language models to detect Parkinsons disease automatically from spontaneous speech with up to 73% accuracy.

Ruturaj Ghatage, Aditya Kulkarni, Rajlaxmi Patil et al.

Question-answering systems have revolutionized information retrieval, but linguistic and cultural boundaries limit their widespread accessibility. This research endeavors to bridge the gap of the absence of efficient QnA datasets in low-resource languages by translating the English Question Answering Dataset (SQuAD) using a robust data curation approach. We introduce MahaSQuAD, the first-ever full SQuAD dataset for the Indic language Marathi, consisting of 118,516 training, 11,873 validation, and 11,803 test samples. We also present a gold test set of manually verified 500 examples. Challenges in maintaining context and handling linguistic nuances are addressed, ensuring accurate translations. Moreover, as a QnA dataset cannot be simply converted into any low-resource language using translation, we need a robust method to map the answer translation to its span in the translated passage. Hence, to address this challenge, we also present a generic approach for translating SQuAD into any low-resource language. Thus, we offer a scalable approach to bridge linguistic and cultural gaps present in low-resource languages, in the realm of question-answering systems. The datasets and models are shared publicly at https://github.com/l3cube-pune/MarathiNLP .

Arslan Chaudhry, Sridhar Thiagarajan, Dilan Gorur

Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these inaccuracies appear confident and convincing. As a result, end-users struggle to consistently align the confidence expressed by LLMs with the accuracy of their predictions, often leading to either blind trust in all outputs or a complete disregard for their reliability. In this work, we explore supervised finetuning on uncertainty-augmented predictions as a method to develop models that produce linguistic expressions of uncertainty. Specifically, we measure the calibration of pre-trained models and then fine-tune language models to generate calibrated linguistic expressions of uncertainty. Through experiments on various question-answering datasets, we demonstrate that LLMs are well-calibrated in assessing their predictions, and supervised finetuning based on the model's own confidence leads to well-calibrated expressions of uncertainty, particularly for single-claim answers.

Dhrubajyoti Pathak, Sanjib Narzary, Sukumar Nandi et al.

Language Processing systems such as Part-of-speech tagging, Named entity recognition, Machine translation, Speech recognition, and Language modeling (LM) are well-studied in high-resource languages. Nevertheless, research on these systems for several low-resource languages, including Bodo, Mizo, Nagamese, and others, is either yet to commence or is in its nascent stages. Language model plays a vital role in the downstream tasks of modern NLP. Extensive studies are carried out on LMs for high-resource languages. Nevertheless, languages such as Bodo, Rabha, and Mising continue to lack coverage. In this study, we first present BodoBERT, a language model for the Bodo language. To the best of our knowledge, this work is the first such effort to develop a language model for Bodo. Secondly, we present an ensemble DL-based POS tagging model for Bodo. The POS tagging model is based on combinations of BiLSTM with CRF and stacked embedding of BodoBERT with BytePairEmbeddings. We cover several language models in the experiment to see how well they work in POS tagging tasks. The best-performing model achieves an F1 score of 0.8041. A comparative experiment was also conducted on Assamese POS taggers, considering that the language is spoken in the same region as Bodo.

Anisa, Neil Amstrong

This research analyzed the persuasion strategies in Grammarly’s advertisements on YouTube. The types of persuasion strategies derived from McPheat's linguistic tools to persuade (2010). This research focused on the linguistic tools to persuade, which include reframing, using someone’s name, mind reading, lost performative, cause and effect relationships, presupposition, universal beliefs, tag questions, and embedded commands.Therefore, this research aims to describe the types of persuasion strategies in Grammarly’s advertisements on YouTube based on McPheat linguistic tools to persuade (2010) theory. This research used a descriptive qualitative method. The source of data was taken from Grammarly’s advertisements published on YouTube inFebruary 2019-Oktober 2022. The researcher collected data by downloading, transcribing, watching videos and reading the script, capturing, conducting data reduction, coding, and then analyzing by presenting, describing, and interpreting the data using McPheat then concludes the data. The results showed that only 7 out of 17 types of persuasion strategies were found in a total of 20 data. Those are presupposition, cause and effect relationships, embedded commands, lost performative, universal beliefs, and reframing: change the time frame, and reframing: appeal to the positive intention behind the belief. The most dominant strategy is presupposition, which features other strategies in one data set. The conclusion is that effective communication relies on a successful exchange of information, and Grammarly achieves this through a diverse range of persuasivestrategies, ensuring its message is well-received by the targeted audiences.

M. Guasti

Ahmad Bsharat

Both Sender & Recipient do communicate through the utilization of signs within a specific context. These signs operate via Grammatical and Stylistic System, which has an essential role in testing meaning more accurately. As a matter of fact, in any communication based on Linguistic Interaction, errors can be corrected, words are modified, and even clarification could be increased. We usually raise inquiries, such as “Do you mean this?” The Recipient to this inquiry may clarify his idea by using more accurate style. But how do tags or signs function in a graphic speech? Can we consider these signs in a Caricature as the intended style? In order to answer these questions, we shall assume that the Semantic Interaction between the signs of a Caricature is the Style, and to test the validity of such assumption, we shall focus on the movement of signs towards the style and ending at the communication process, since the latter is deemed a critical point in testing meaning.