Hasil untuk "English language"

N. Flores, J. Rosa

Amy Beth Warriner, V. Kuperman, M. Brysbaert

Rosina L. Lippi-Green

Preface Introduction: Language Ideology or Science Fiction? 1. The Linguistic Facts of Life 2. Language in Motion 3. The Myth of Non-Accent 4. The Standard Language Myth 5. Language Subordination 6. The Educational System: Fixing the Message in Stone 7. Teaching Children How to Discriminate (What We Learn From the Big Bad Wolf) 8. The Information Industry 9. Real People with a Real Language: The Workplace and the Judicial System 10. The Real Trouble with Black English 11. Hillbillies, Hicks & Southern Belles: The Language Rebels 12. Defying Paradise: Hawai'i 13. The Other In The Mirror 14. !Ya Basta! 15. The Unassimilable Races: What It Means To Be Asian 16. Case Study: Moral Panic in Oakland 17. Case Study: Linguistic Profiling and Fair Housing 18. Conclusions: Civil (Dis)obedience And The Shadow of Language Glossary Bibliography I Bibliography II

Marine Holborow

T. Mcarthur, Feri McArthur

F. T. Visser

The aim of this study is to provide an outline of the development, from the earliest times to the present day, of all the English syntactical constructions with a verbal form as their nucleus. Professor Visser's description is based on a very extensive collection of documentary material covering every kind of writing in prose and poetry in the Old, Middle and Modern periods, drawing on quotations illustrating syntactical pheonomena in Bosworth & Toller, OED, MMED, EDD and DOST, but also making reference to obsolete usages not found in any grammar, and to the views of English and American grammarians of the 17th, 18th and 19th centuries on the various syntactical constructions. The volumes of this work originally appeared in the early 60s and 70s and were well received by readers and reviewers. Volumes 1 and 2 have undergone correction in the light of these early reactions.

Holger Schwenk, Vishrav Chaudhary, Shuo Sun et al.

We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We do not limit the extraction process to alignments with English, but we systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 16720 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English.

D. August, María S. Carlo, C. Dressler et al.

María S. Carlo, D. August, B. Mclaughlin et al.

Li Jiang, L. Zhang, Stephen May

J. Cenoz, D. Gorter

Funding information MINECO/FEDER,Grant/AwardNumber: EDU2015-63967-R; Eusko Jaurlaritza, Grant/AwardNumber:DREAMIT1225-19 Abstract Teaching English has traditionally been associated with a monolingual bias and the exclusive use of English in the classroom is highly recommended in different countries. Nowadays English is widely used to teach academic content and this strict separation of languages can be problematic because it prevents students from using resources they have previously acquired in other languages (Cenoz & Gorter, 2015; Kubota, 2018). In this article we discuss ‘pedagogical translanguaging’ understood as intentional instructional strategies that integrate two ormore languages and aim at the development of the multilingual repertoire as well as metalinguistic and language awareness. Pedagogical translanguaging considers learners as emergent multilinguals who can use English and other languages depending on the social context. Their linguistic resources are valued and learners are not seen as deficient users of English but as multilingual speakers.

Ya-Ting Carolyn Yang, Yi-Chien Chen, Hsiu-Ting Hung

Abstract The present research examined the effectiveness of digital storytelling (DST) on foreign language learners’ English speaking and creative thinking. In this study, DST was realized in the form of an interdisciplinary project integrated in a partnership between an English course and a computer course, with the class time of the former devoted to the content design and that of the latter to the multimedia design of learner-generated digital stories. The participants were required to work in small groups to create their digital stories in the target language, English, under an eight-week interdisciplinary curriculum. A two-group quasi-experiment with a pretest and posttest design was then conducted to compare the participants’ learning outcomes. The findings reveal the authentic and meaningful learning opportunities that DST has to offer for effectively fostering the students’ development of becoming proficient English speakers and creative thinkers. Future implementations on interdisciplinary DST projects are thus recommended for educators.

Paul Bontempo

This paper investigates the relationship between utterance sentiment and language choice in English-Tamil code-switched text, using methods from machine learning and statistical modelling. We apply a fine-tuned XLM-RoBERTa model for token-level language identification on 35,650 romanized YouTube comments from the DravidianCodeMix dataset, producing per-utterance measurements of English proportion and language switch frequency. Linear regression analysis reveals that positive utterances exhibit significantly greater English proportion (34.3%) than negative utterances (24.8%), and mixed-sentiment utterances show the highest language switch frequency when controlling for utterance length. These findings support the hypothesis that emotional content demonstrably influences language choice in multilingual code-switching settings, due to socio-linguistic associations of prestige and identity with embedded and matrix languages.

Xinyue Ma, Pol Pastells, Mireia Farrús et al.

Machine Translation (MT) evaluation has gone beyond metrics, towards more specific linguistic phenomena. Regarding English-Chinese language pairs, passive sentences are constructed and distributed differently due to language variation, thus need special attention in MT. This paper proposes a bidirectional multi-domain dataset of passive sentences, extracted from five Chinese-English parallel corpora and annotated automatically with structure labels according to human translation, and a test set with manually verified annotation. The dataset consists of 73,965 parallel sentence pairs (2,358,731 English words, 3,498,229 Chinese characters). We evaluate two state-of-the-art open-source MT systems with our dataset, and four commercial models with the test set. The results show that, unlike humans, models are more influenced by the voice of the source text rather than the general voice usage of the source language, and therefore tend to maintain the passive voice when translating a passive in either direction. However, models demonstrate some knowledge of the low frequency and predominantly negative context of Chinese passives, leading to higher voice consistency with human translators in English-to-Chinese translation than in Chinese-to-English translation. Commercial NMT models scored higher in metric evaluations, but LLMs showed a better ability to use diverse alternative translations. Datasets and annotation script will be shared upon request.

Luis Javier Pentón Herrera, Ana Maria Ferreira Barcelos, Yasir Hussain

Abstract: This article introduces the concept of affective multiliteracies as a new framework for English language education. While current approaches to multiliteracies have expanded the scope of literacy to include multimodal, cultural, and digital dimensions, they continue to emphasize what we define as outer literacies: visible, assessable, and performative skills used to engage with texts, tools, and external contexts. This emphasis often overshadows inner literacies, or the social, emotional, and relational capacities that shape how learners interpret experiences and connect with others. In response, we propose affective multiliteracies as a way to harmonize these inner and outer dimensions, positioning literacy as both internal meaning-making and external participation. Drawing on research in emotional intelligence, pedagogical love, and harmony, this paper examines how educators can cultivate learning environments that support students as human beings. We provide theoretical grounding, practical illustrations, and pedagogical implications to support this holistic vision. By rethinking literacy as a human practice shaped by both cognitive and affective processes, this approach aims to guide educators in preparing students to communicate thoughtfully, relate ethically, and participate meaningfully in their communities worldwide.

Jarosław A. Chudziak, Michał Wawer

This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, and autonomous decision-making. Experiments demonstrate the system's effectiveness in pattern recognition and generating natural language descriptions of market trends. ElliottAgents contributes to NLP applications in specialized domains, showcasing how AI-driven dialogue systems can enhance collaborative analysis in data-intensive fields. This research bridges the gap between complex financial data and human understanding, addressing the need for interpretable and adaptive prediction systems in finance.

Zaur Gouliev, Jennifer Waters, Chengqian Wang

Disinformation spreads rapidly across linguistic boundaries, yet most AI models are still benchmarked only on English. We address this gap with a systematic comparison of five multilingual transformer models: mBERT, XLM, XLM-RoBERTa, RemBERT, and mT5 on a common fake-vs-true machine learning classification task. While transformer-based language models have demonstrated notable success in detecting disinformation in English, their effectiveness in multilingual contexts still remains up for debate. To facilitate evaluation, we introduce PolyTruth Disinfo Corpus, a novel corpus of 60,486 statement pairs (false claim vs. factual correction) spanning over twenty five languages that collectively cover five language families and a broad topical range from politics, health, climate, finance, and conspiracy, half of which are fact-checked disinformation claims verified by an augmented MindBugs Discovery dataset. Our experiments revealed performance variations. Models such as RemBERT achieved better overall accuracy, particularly excelling in low-resource languages, whereas models like mBERT and XLM exhibit considerable limitations when training data is scarce. We provide a discussion of these performance patterns and implications for real-world deployment. The dataset is publicly available on our GitHub repository to encourage further experimentation and advancement. Our findings illuminate both the potential and the current limitations of AI systems for multilingual disinformation detection.

Manish Pandey, Nageshwar Prasad Yadav, Mokshada Adduru et al.

With the growing presence of multilingual users on social media, detecting abusive language in code-mixed text has become increasingly challenging. Code-mixed communication, where users seamlessly switch between English and their native languages, poses difficulties for traditional abuse detection models, as offensive content may be context-dependent or obscured by linguistic blending. While abusive language detection has been extensively explored for high-resource languages like English and Hindi, low-resource languages such as Telugu and Nepali remain underrepresented, leaving gaps in effective moderation. In this study, we introduce a novel, manually annotated dataset of 2 thousand Telugu-English and 5 Nepali-English code-mixed comments, categorized as abusive and non-abusive, collected from various social media platforms. The dataset undergoes rigorous preprocessing before being evaluated across multiple Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs). We experimented with models including Logistic Regression, Random Forest, Support Vector Machines (SVM), Neural Networks (NN), LSTM, CNN, and LLMs, optimizing their performance through hyperparameter tuning, and evaluate it using 10-fold cross-validation and statistical significance testing (t-test). Our findings provide key insights into the challenges of detecting abusive language in code-mixed settings and offer a comparative analysis of computational approaches. This study contributes to advancing NLP for low-resource languages by establishing benchmarks for abusive language detection in Telugu-English and Nepali-English code-mixed text. The dataset and insights can aid in the development of more robust moderation strategies for multilingual social media environments.

Boraso, Silvia

Prachi Jain, Manu Rathee, Arush Bansal et al.

Background: Oral health plays a crucial role in maintaining the general health of an individual. Parkinson disease (PD) has known to disrupt the oral functions. Prosthetic rehabilitation can be done in these patients. However, there is scarcity of literature to assess the effectiveness or impact of rehabilitation with prosthesis either fixed or removable on various oral functions and quality of life (QoL) or satisfaction of PD patients. The purpose of this systematic study was to assess the effectiveness of prosthodontic rehabilitation in patients with PD. Materials and Methods: The literature search was conducted in the PubMed and CINAHL database for the articles till 2024 in English language. An exploration of gray literature was also included through Google Scholar. Manual search in the references of the selected articles was also done for relevant articles. The methodological quality assessment of cohort studies was done using Newcastle–Ottawa quality assessment form for Cohort Studies (NOS). Assessment of cross-sectional studies was done using the Appraisal tool for Cross-Sectional Studies (tool) and aassessment of case series was done using JBI critical appraisal tool for case series. Results: A total of 6 articles were selected from PubMed, 1 from CINAHL, and 2 from Google Scholar. Four articles studied the masticatory efficiency. Oral perception and motor ability were analyzed in two articles. Oral Health QoL was assessed in four articles. One article studied the electromyographic activity. Conclusion: Based on this systematic review, it can be suggested that prosthetic rehabilitation using fixed or removable prosthesis offer potential benefits in PD patients improving the oral functions and QoL. However, there is a dearth of long-term research on evaluation of impact of prosthetic rehabilitation in improving the oral function and QoL of PD patients. PROSPERO Registration: CRD42024570296.