Hasil untuk "English literature"

Michal Keller

Technical language is not literary in the eyes of many critics; it tends to exclude readers and so we often leave it unread on the page, as ornament or reality effect. This chapter suggests that technical language is literary language and that understanding what it signifies both literally and in relation to what we (too confidently) think of as “standard” language can take us down various philological, critical, and political paths of reading. Our exemplary text is The Return of the Native, in which Hardy deploys a “Wessex” dialect that is always surrounded by standard English. Indeed, dialect is a particularly problematic technical language because it is so local, and often connotes the claustrophobia of the regional. At the same time, it imparts to us a knowledge of place and the means of survival in various places that does crucial work, in literature as well as out of it.

Alvons Habibie, Harto S. Malik

Background: The preservation of local literature rooted in oral traditions faces new challenges and opportunities in the digital era. Digital technologies enable documentation and wider dissemination, yet they also raise concerns about cultural authenticity, community sovereignty, and sustainability. A systematic synthesis is required to clarify current scholarly directions. Aims: This study aims to: (1) identify dominant themes and research approaches in recent studies on the digital preservation of local literatures; (2) examine how scholars conceptualize the shift from oral to digital forms; and (3) analyze the documented educational impact of digitization within indigenous and local communities. Methods: A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines. Searches across Scopus, Google Scholar, EBSCOhost, and ProQuest covered English and Indonesian publications from 2010–2025. Studies focused on preserving local literary content were included, while purely technical works were excluded. Data were synthesized through thematic analysis. Results: Six thematic clusters emerged: Digital Tools and Innovation; Community Participation and Co-Creation; Ethical Access and Cultural Protocols; Multimedia Storytelling; Digital Knowledge Management; and Collaborative Institutional Models. Scholars largely frame digital transition as cultural continuity and narrative hybridity shaped by technology but grounded in community stewardship and digital sovereignty. Educationally, digitization enhances reading comprehension, writing skills, engagement, accessibility, and cultural relevance. Implications: Effective preservation requires culturally grounded, participatory, and multidisciplinary approaches. Policymakers and practitioners should strengthen community-led digital stewardship, establish ethical access guidelines, and integrate digitized local literatures into curricula for sustainable cultural continuity.

Hasan Can Biyik, Libby Barak, Jing Peng et al.

Euphemisms substitute socially sensitive expressions, often softening or reframing meaning, and their reliance on cultural and pragmatic context complicates modeling across languages. In this study, we investigate how cross-lingual equivalence influences transfer in multilingual euphemism detection. We categorize Potentially Euphemistic Terms (PETs) in Turkish and English into Overlapping (OPETs) and Non-Overlapping (NOPETs) subsets based on their functional, pragmatic, and semantic alignment. Our findings reveal a transfer asymmetry: semantic overlap is insufficient to guarantee positive transfer, particularly in low-resource Turkish-to-English direction, where performance can degrade even for overlapping euphemisms, and in some cases, improve under NOPET-based training. Differences in label distribution help explain these counterintuitive results. Category-level analysis suggests that transfer may be influenced by domain-specific alignment, though evidence is limited by sparsity.

Claire A Richards, Ann Dyer, Melissa Vera et al.

This scoping review maps nurse scientists’ contributions to climate change and health research, including work about Indigenous Peoples, and identifies gaps and future opportunities. A review was conducted and reported using the PRISMA extension for scoping reviews, including a systematized literature search. Eligible articles were English-language studies published between 2018–2023, in nursing journals or by nursing faculty, and related to climate change or associated extreme weather events. Abstracted data included: Year, Focus (e.g. multiple hazards, sustainability), Outcomes, Social Determinants of Health, Sample Population/Setting, Study Design, Study Location, and Field of Journal. Studies were categorized by the countries’ Sustainable Development Index (SDI) to evaluate equity in representation. Two reviewers screened the first 30 abstracts for consistency. Remaining articles were screened independently, with discrepancies resolved through discussion. Overall, 202 articles were included, with 159 reporting primary research. Publications on climate change increased over time, with 66% on climate hazards, 29% on broader climate change or sustainability themes, and 5% on other topics. Nearly half (42%) were conducted in countries with low SDI scores (<0.250). Adults and healthcare providers were the most frequent populations sampled, with few studies of communities, work, or school settings. Nearly half were observational studies (44%), followed by qualitative inquiry (22%), with little interventional or community-engaged research. Few (4%) focused on Indigenous health and 42% addressed at least one social determinant of health. Physical health, mental health, and risk management were the most common outcomes; few examined systems of power in adjusting to climate change. We found many opportunities to strengthen and increase nursing research on climate change, including by emphasizing local and global factors shaping climate vulnerability, engaging diverse ways of knowing, centring Indigenous knowledges, studying sustainability and a just energy transition, and pursuing solutions-oriented, transformative research across more diverse populations and settings.

Tom S Juzek

We present a preview of the Syntactic Acceptability Dataset, a resource being designed for both syntax and computational linguistics research. In its current form, the dataset comprises 1,000 English sequences from the syntactic discourse: Half from textbooks and half from the journal Linguistic Inquiry, the latter to ensure a representation of the contemporary discourse. Each entry is labeled with its grammatical status ("well-formedness" according to syntactic formalisms) extracted from the literature, as well as its acceptability status ("intuitive goodness" as determined by native speakers) obtained through crowdsourcing, with highest experimental standards. Even in its preliminary form, this dataset stands as the largest of its kind that is publicly accessible. We also offer preliminary analyses addressing three debates in linguistics and computational linguistics: We observe that grammaticality and acceptability judgments converge in about 83% of the cases and that "in-betweenness" occurs frequently. This corroborates existing research. We also find that while machine learning models struggle with predicting grammaticality, they perform considerably better in predicting acceptability. This is a novel finding. Future work will focus on expanding the dataset.

Nabeelah Faumi, Adeepa Gunathilake, Benura Wickramanayake et al.

With the advent of Web 2.0, the development in social technology coupled with global communication systematically brought positive and negative impacts to society. Copyright claims and Author identification are deemed crucial as there has been a considerable amount of increase in content violation owing to the lack of proper ethics in society. The Author's attribution in both English and Romanized Sinhala became a major requirement in the last few decades. As an area largely unexplored, particularly within the context of Romanized Sinhala, the research contributes significantly to the field of computational linguistics. The proposed author attribution system offers a unique approach, allowing for the comparison of only two sets of text: suspect author and anonymous text, a departure from traditional methodologies which often rely on larger corpora. This work focuses on using the numerical representation of various pairs of the same and different authors allowing for, the model to train on these representations as opposed to text, this allows for it to apply to a multitude of authors and contexts, given that the suspected author text, and the anonymous text are of reasonable quality. By expanding the scope of authorship attribution to encompass diverse linguistic contexts, the work contributes to fostering trust and accountability in digital communication, especially in Sri Lanka. This research presents a pioneering approach to author attribution in both English and Romanized Sinhala, addressing a critical need for content verification and intellectual property rights enforcement in the digital age.

Valleron, Alain-Jacques

Biosecurity is a word including, in French, two concepts which are differently expressed in the English literature: the biosafety (to prevent accidental releases of infectious agents, for example, in research laboratories) and the biosecurity (to prevent the voluntary dispersion of infectious agents by bioterrorists, for example). Up to now, bioterrorism using a biological weapon has been very rare. Epidemiological surveillance is frequently seen as an efficient tool to achieve a better biosafety, and increase biosecurity. However, biosecurity requires an alert system. Traditional surveillance, which was developed to help to design the health policies, lacks the sensitivity and rapid response delay necessary for alert. Biology brings new opportunities for alert and surveillance. Examples are the monitoring of sewer systems and the development of CRISPR tools for detection of biological agents. A new problem is that these tools can be used out of the research laboratories, because they are relatively cheap and easy to develop. Finally, whatever the method used to identify quickly a new hazard, the key problem is the “preparedness” to identify and connect quickly the biomedical experts able to provide the best response to this hazard.

Mohammad Nazmush Shamael, Sabila Nawshin, Swakkhar Shatabda et al.

This work presents the BanglishRev Dataset, the largest e-commerce product review dataset to date for reviews written in Bengali, English, a mixture of both and Banglish, Bengali words written with English alphabets. The dataset comprises of 1.74 million written reviews from 3.2 million ratings information collected from a total of 128k products being sold in online e-commerce platforms targeting the Bengali population. It includes an extensive array of related metadata for each of the reviews including the rating given by the reviewer, date the review was posted and date of purchase, number of likes, dislikes, response from the seller, images associated with the review etc. With sentiment analysis being the most prominent usage of review datasets, experimentation with a binary sentiment analysis model with the review rating serving as an indicator of positive or negative sentiment was conducted to evaluate the effectiveness of the large amount of data presented in BanglishRev for sentiment analysis tasks. A BanglishBERT model is trained on the data from BanglishRev with reviews being considered labeled positive if the rating is greater than 3 and negative if the rating is less than or equal to 3. The model is evaluated by being testing against a previously published manually annotated dataset for e-commerce reviews written in a mixture of Bangla, English and Banglish. The experimental model achieved an exceptional accuracy of 94\% and F1 score of 0.94, demonstrating the dataset's efficacy for sentiment analysis. Some of the intriguing patterns and observations seen within the dataset and future research directions where the dataset can be utilized is also discussed and explored. The dataset can be accessed through https://huggingface.co/datasets/BanglishRev/bangla-english-and-code-mixed-ecommerce-review-dataset.

Josh Brown, Yutong Bu, Zachary Cheesman et al.

In this research, we examine the capabilities of different mathematical models to accurately predict various levels of the English football pyramid. Existing work has largely focused on top-level play in European leagues; however, our work analyzes teams throughout the entire English Football League system. We modeled team performance using weighted Colley and Massey ranking methods which incorporate player valuations from the widely-used website Transfermarkt to predict game outcomes. Our initial analysis found that lower leagues are more difficult to forecast in general. Yet, after removing dominant outlier teams from the analysis, we found that top leagues were just as difficult to predict as lower leagues. We also extended our findings using data from multiple German and Scottish leagues. Finally, we discuss reasons to doubt attributing Transfermarkt's predictive value to wisdom of the crowd.

Dipankar Srirag, Aditya Joshi, Jordan Painter et al.

Despite large language models (LLMs) being known to exhibit bias against non-standard language varieties, there are no known labelled datasets for sentiment analysis of English. To address this gap, we introduce BESSTIE, a benchmark for sentiment and sarcasm classification for three varieties of English: Australian (en-AU), Indian (en-IN), and British (en-UK). We collect datasets for these language varieties using two methods: location-based for Google Places reviews, and topic-based filtering for Reddit comments. To assess whether the dataset accurately represents these varieties, we conduct two validation steps: (a) manual annotation of language varieties and (b) automatic language variety prediction. Native speakers of the language varieties manually annotate the datasets with sentiment and sarcasm labels. We perform an additional annotation exercise to validate the reliance of the annotated labels. Subsequently, we fine-tune nine LLMs (representing a range of encoder/decoder and mono/multilingual models) on these datasets, and evaluate their performance on the two tasks. Our results show that the models consistently perform better on inner-circle varieties (i.e., en-AU and en-UK), in comparison with en-IN, particularly for sarcasm classification. We also report challenges in cross-variety generalisation, highlighting the need for language variety-specific datasets such as ours. BESSTIE promises to be a useful evaluative benchmark for future research in equitable LLMs, specifically in terms of language varieties. The BESSTIE dataset is publicly available at: https://huggingface.co/ datasets/unswnlporg/BESSTIE.

Patrycja Strycharczuk, Sam Kirkham, Emily Gorman et al.

Diphthong vowels exhibit a degree of inherent dynamic change, the extent of which can vary synchronically and diachronically, such that diphthong vowels can become monophthongs and vice versa. Modelling this type of change requires defining diphthongs in opposition to monophthongs. However, formulating an explicit definition has proven elusive in acoustics and articulation, as diphthongisation is often gradient in these domains. In this study, we consider whether diphthong vowels form a coherent phonetic category from the articulatory point of view. We present articulometry and acoustic data from six speakers of Northern Anglo-English producing a full set of phonologically long vowels. We analyse several measures of diphthongisation, all of which suggest that diphthongs are not categorically distinct from long monophthongs. We account for this observation with an Articulatory Phonology/Task Dynamic model in which diphthongs and long monophthongs have a common gestural representation, comprising two articulatory targets in each case, but they differ according to gestural constriction and location of the component gestures. We argue that a two-target representation for all long vowels is independently supported by phonological weight, as well as by the nature of historical diphthongisation and present-day dynamic vowel variation in British English.

David R. Mortensen, Valentina Izrailevitch, Yunze Xiao et al.

Lexical-syntactic flexibility, in the form of conversion (or zero-derivation) is a hallmark of English morphology. In conversion, a word with one part of speech is placed in a non-prototypical context, where it is coerced to behave as if it had a different part of speech. However, while this process affects a large part of the English lexicon, little work has been done to establish the degree to which language models capture this type of generalization. This paper reports the first study on the behavior of large language models with reference to conversion. We design a task for testing lexical-syntactic flexibility -- the degree to which models can generalize over words in a construction with a non-prototypical part of speech. This task is situated within a natural language inference paradigm. We test the abilities of five language models -- two proprietary models (GPT-3.5 and GPT-4), three open-source models (Mistral 7B, Falcon 40B, and Llama 2 70B). We find that GPT-4 performs best on the task, followed by GPT-3.5, but that the open source language models are also able to perform it and that the 7B parameter Mistral displays as little difference between its baseline performance on the natural language inference task and the non-prototypical syntactic category task, as the massive GPT-4.

Hongfa Zi, Zhen Liu

The characters used in modern countries are mainly divided into ideographic characters and phonetic characters, both of which have their advantages and disadvantages. Chinese is difficult to learn and easy to master, while English is easy to learn but has a large vocabulary. There is still no language that combines the advantages of both languages and has less memory capacity, can form words, and is easy to learn. Therefore, inventing new characters that can be combined and the popularization of deep knowledge, and reduce disputes through communication. Firstly, observe the advantages and disadvantages of Chinese and English, such as their vocabulary, information content, and ease of learning in deep scientific knowledge, and create a new writing system. Then, use comparative analysis to observe the total score of the new language. Through this article, it can be concluded that the new text combines the advantages of both pictographic and alphabetical writing: new characters that can be combined into words reduces the vocabulary that needs to be learned; Special prefixes allow beginners to quickly guess the approximate category and meaning of unseen words; New characters can enable humans to quickly learn more advanced knowledge.

Avinash S. Bidra, T. Taylor, J. Agar

M. Lestari, Achmad Yudi Wahyudin

This study attempts to explore the language learning strategies used by the students’ who take English Literature study programs in English as a foreign language (EFL) setting. This study involves 76 participants asked to fulfill a questionnaire called Strategy Inventory for Language Learning (SILL) developed by Oxford (1990). The result of this research showed that metacognitive has been the most frequently used strategy followed by social and compensation strategies while affective strategies become the least strategy used by the students. This research could be meaningful insight for other researches or the students to analyze the language learning strategies used by the students and be meaningful to know the language learning strategies that appropriate especially in the field of the second language.

G. Valdés

Elda Nisya Auliainsani, Harwintha Yuhria Anjarningsih

Many people have attempted to criminalize the LGBT community. This study aims to examine how @WhatIsUpIndonesia negotiates the supported ideology with the dominant ideology in their Instagram posts about criminalizing LGBT people through two different cases; the proposed revision of Indonesia's Criminal Code (RKUHP) and Bogor’s Regional Regulation on the Prevention and Countermeasures Against Sexually Deviant Behavior. A corpus of two posts about the two cases is analyzed using social semiotic multimodal analysis in two steps: textual analysis and visual analysis. This study finds that WIUI negotiates its relatively liberal values with the dominant conservative ideology in Indonesia by choosing ambivalence through the shifting focus and overgeneralizing the issue using recontextualization and memes. In conclusion, two opposing ideologies in social media activism can be negotiated using ambivalence instead of leaning towards only one. However, the limitations of this research prevented a thorough examination of how WIUI interacts with its audience.

Kaveh Jalilzadeh, Adel Dastgoshadeh, Raheleh Khosravi

This research explores language assessment practices and training preferences in Iranian English as a Foreign Language (EFL) teaching, aiming to provide valuable insights into the current landscape among 363 Iranian EFL teachers. Data collection included diverse demographics, facilitating a thorough analysis of assessment practices and preferences. Statistical analyses, such as chi-square tests, revealed a significant gap between the perceived importance and the actual proficiency of Iranian EFL teachers. Speaking skills are prioritized while listening comprehension is least emphasized. Common assessment methods include active class participation, oral presentations, and closed-ended tests, with underutilized methods suggesting a need for broader teacher development programs. The study underscores the diverse terminology used for teacher-mediated assessments, emphasizing the multifaceted nature of EFL assessment practices. In summary, the paper highlights the significance of tailored assessment literacy programs to bridge the gap and enhance English language teaching in Iran.

Alina R. Kasimova, Ekaterina M. Gordina, Sergey S. Toropov et al.

Background. Stenotrophomonas maltophilia (S. maltophilia) is a gram-negative non-fermenting bacillus and is a rare pathogen of orthopedic infection. Due to the relatively low virulence of S. maltophilia, many clinicians are still faced with the question of whether this bacterial species is simply a colonizing agent or the true cause of infection. Aim of the study to raise the awareness of practitioners about S. maltophilia as a rare pathogen of orthopedic infection. Methods. A retrospective analysis was performed concerning the frequency of S. maltophilia isolation from patients treated at the Vreden Center for periprosthetic infection and/or osteomyelitis from January 1, 2009 to October 31, 2022. The literature search by keywords was carried out in the PubMed/MEDLINE, Scopus, eLIBRARY, and Cyberleninka databases. The search retrieved 587 articles published in Russian or English over the period from 2012 to November 2022. Results. During the study period, 9 cases of orthopedic monoinfection with S. maltophilia were identified in 9 patients aged 36 to 83 years. At the time of admission, no leukocytosis was detected in patients, and only 2 of 9 patients had elevated C-reactive protein level. S. maltophilia is naturally resistant to many broad-spectrum antibiotics. Co-trimoxazole is considered the drug of choice for the treatment of S. maltophilia infection. The limited choice of drugs for targeted therapy, the presence of multiple determinants of antibiotic resistance, the existence of microbial associations and patient risks including implantation, chronic nature of infection, elderly age, as well as the presence of significant concomitant somatic pathology can lead to the ineffectiveness of the ongoing treatment of infections caused by S. maltophilia. Our experience shows that in the case of sensitivity of S. maltophilia strain to co-trimoxazole it is possible to prescribe this drug for a long course as monotherapy, provided that the radical surgical treatment of the focus is performed.

Kasun Wickramasinghe, Nisansa de Silva

Since their inception, embeddings have become a primary ingredient in many flavours of Natural Language Processing (NLP) tasks supplanting earlier types of representation. Even though multilingual embeddings have been used for the increasing number of multilingual tasks, due to the scarcity of parallel training data, low-resource languages such as Sinhala, tend to focus more on monolingual embeddings. Then when it comes to the aforementioned multi-lingual tasks, it is challenging to utilize these monolingual embeddings given that even if the embedding spaces have a similar geometric arrangement due to an identical training process, the embeddings of the languages considered are not aligned. This is solved by the embedding alignment task. Even in this, high-resource language pairs are in the limelight while low-resource languages such as Sinhala which is in dire need of help seem to have fallen by the wayside. In this paper, we try to align Sinhala and English word embedding spaces based on available alignment techniques and introduce a benchmark for Sinhala language embedding alignment. In addition to that, to facilitate the supervised alignment, as an intermediate task, we also introduce Sinhala-English alignment datasets. These datasets serve as our anchor datasets for supervised word embedding alignment. Even though we do not obtain results comparable to the high-resource languages such as French, German, or Chinese, we believe our work lays the groundwork for more specialized alignment between English and Sinhala embeddings.