Linguistic Features Research Articles

Adolescent English Learners’ Awareness of Disciplinary Writing and Academic Language in Science Classrooms: The Affordances of a Register-Functional Perspective

ABSTRACT This article reports findings from a study that investigated English learners’ (ELs’) developing awareness of disciplinary writing and academic language in science courses in a U.S.-based high school. Based on an analysis of writing samples, this case study first introduces the situational and linguistic characteristics of two science writing tasks in forensics and oceanography. The author then explores ELs’ developing understanding of science writing and academic science language via ethnographically oriented interviews. Informed by the Register-Functional perspective and Academic Literacies frameworks, the analyses demonstrate higher EL sensitivity to vocabulary, but not grammatical structures yet. Writing practices in science classes shape ELs’ understanding of disciplinary writing in unexpected directions. The situational analysis of writing and ELs’ perspectives provide a systematic approach for understanding learner needs in writing and integrating language into content instruction. Implications for EL education in science and teacher collaborations are provided.

STEM authorship, user-generated reviews and their impact: legitimate or not?

STEM authorship, user-generated reviews and their impact: legitimate or not?

Evaluating Transformers and Linguistic Features integration for Author Profiling tasks in Spanish

Author profiling consists of extracting their demographic and psychographic information by examining their writings. This information can then be used to improve the reader experience and to detect bots or propagators of hoaxes and/or hate speech. Therefore, author profiling can be applied to build more robust and efficient Knowledge-Based Systems for tasks such as content moderation, user profiling, and information retrieval. Author profiling is typically performed automatically as a document classification task. Recently, language models based on transformers have also proven to be quite effective in this task. However, the size and heterogeneity of novel language models, makes it necessary to evaluate them in context. The contributions we make in this paper are four-fold: First, we evaluate which language models are best suited to perform author profiling in Spanish. These experiments include basic, distilled, and multilingual models. Second, we evaluate how feature integration can improve performance for this task. We evaluate two distinct strategies: knowledge integration and ensemble learning. Third, we evaluate the ability of linguistic features to improve the interpretability of the results. Fourth, we evaluate the performance of each language model in terms of memory, training, and inference times. Our results indicate that the use of lightweight models can indeed achieve similar performance to heavy models and that multilingual models are actually less effective than models trained with one language. Finally, we confirm that the best models and strategies for integrating features ultimately depend on the context of the task.

Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian language

Nowadays, the spread of deceptive reviews is a problem that has reached critical dimensions, having a significant economic impact on business activities. This paper aims to estimate - at the quantitative and qualitative levels - the possibility of using particular words to disambiguate between truthful and deceptive text, focusing on reviews produced in the cultural heritage domain. For this purpose, a lexicon-based methodology has used two different lexicons: sentiment information, intensifiers, downtoners, and negation operators. As known in the literature, these elements are crucial in a classification process related to deceptiveness. The evaluation phase has considered quantitative metrics such as accuracy and F1 score and ad hoc developed metrics that consider specific linguistic parameters such as polarity and tone of voice intensifiers. A qualitative analysis of a subset of the corpus has also been carried out to understand better factors that impact the classification of deceptive review. Several linguistic features have been considered, ranging from the number of intensifiers to their type and position in phrases and sentences. A comparison between the performances of two different lexicons used has been added to the analysis.

The Talyshis: An Iranian people divided by the Araxes

This paper examines the Talyshi people, an Iranian ethnic group divided by the Araxes River between Azerbaijan and Iran. The division, a result of the Russo-Persian War (1804-1813), placed the Talyshis within two distinct political and cultural paradigms. The northern Talyshis, under the successive rule of the Russian Empire, the Soviet Union and Azerbaijan, were confronted with a Turkish political and cultural orientation that contrasted with their Iranian linguistic and cultural heritage. This orientation led to significant shifts in identity, with the Talyshis becoming minorities within a Turkic-speaking, Turkish-oriented Azerbaijan. Southern Talyshis who remained in Iran experienced a more consistent cultural development within the Iranian paradigm, despite political changes. This study examines the impact of these divisions on Talyshi identity, language preservation and socio-cultural adaptation. It discusses the challenges of assimilation and cultural preservation faced by the Talyshis, their linguistic characteristics, and the revival of Talysh self-awareness and nationalism in the face of external pressures. The paper uses a comprehensive analysis of historical, linguistic and ethnographic data to provide insights into the resilience of Talysh identity amidst political and cultural change.

Context Disentangling and Prototype Inheriting for Robust Visual Grounding.

Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the target from other objects, particularly for the targets that have the same category as others. However, most previous methods underestimate such information. Moreover, they are usually designed for the standard scene (without any novel object), which limits their generalization to the open-vocabulary scene. In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes. Specifically, the context disentangling disentangles the referent and context features, which achieves better discrimination between them. The prototype inheriting inherits the prototypes discovered from the disentangled visual features by a prototype bank to fully utilize the seen data, especially for the open-vocabulary scene. The fused features, obtained by leveraging Hadamard product on disentangled linguistic and visual features of prototypes to avoid sharp adjusting the importance between the two types of features, are then attached with a special token and feed to a vision Transformer encoder for bounding box regression. Extensive experiments are conducted on both standard and open-vocabulary scenes. The performance comparisons indicate that our method outperforms the state-of-the-art methods in both scenarios.

Are Orthopaedic Clinical Trials Linguistically and Culturally Diverse?

Background: Underrepresentation and misrepresentation of historically underrepresented populations in randomized controlled trials (RCTs) may have implications for the validity of research results and their application to diverse populations. The purpose of this study was to evaluate the representation of historically linguistically, racially, and ethnically underrepresented participants in orthopaedic RCTs and to assess the use of translated and culturally adapted patient-reported outcome measures (PROMs). Methods: Separate and comprehensive literature searches of PubMed, Web of Science, and Embase databases were performed to identify RCTs using PROMs between 2012 and 2022 among the top 5 highest 5-year impact factor orthopaedic journals according to the 2021 Journal Citation Reports database. The primary outcomes of interest included reporting of linguistic, racial, and ethnic demographic characteristics of trial participants and the utilization of translated PROMs. The methodological quality of each clinical trial was assessed using the Jadad Criteria. Results: A total of 230 RCTs met inclusion criteria. The language of participants was reported in 14% of trials and in 17% of trials when searching both the published text and clinical trial registration information. In addition, race and/or ethnicity was reported in 11% of trials, and the use of translated PROMs was reported in 7% of trials. Among the 6 multinational studies, none reported the language of the study population, nor the use of translated PROMs. Notably, 4 studies (2%) reported using culturally adapted PROMs. The average Jadad score was 3.07. Conclusion: Participant language, race, and ethnicity are infrequently reported in orthopaedic clinical trials, potentially limiting the application and interpretation of study results. Similarly, the linguistic and cultural adaptation of PROMs used is often not reported, which also limits interpretations of the validity and generalizability of orthopedic study results. Researchers and journals should promote standard reporting of demographic data and methods of PROM adaptation to ensure that results are generalizable to diverse patient populations. Level of Evidence: Level III. See Instructions for Authors for a complete description of levels of evidence.

Investigating the Genuineness of Suicide Notes in Social Media

This study aims to find linguistic evidence that is able to act as legal evidence in determining the genuineness of a suicide note. The data sources were written and digital suicide notes from Indonesia. The data were collected from social media through documentation. The data collected were graphology and lingual units that indicate the linguistic features of a genuine suicide note. The units were seen from their structure, meaning, and context to determine the motivation. The data were analyzed through referential identity, orthography identity, and distributional methods from descriptive-qualitative and descriptive-quantitative approaches based on linguistic theories. The theories applied were intertextuality, meaning, linguistic features of suicide notes, the context of the situation, and generative transformation. The findings show that written and digital suicide notes, as well as posts on the victim’s social media, provide evidence that the note is genuine and written by the victim. The evidences are recursive graphology, interrelated propositions, and linguistic features of a genuine suicide note. The motivations for suicide are derived from the victim’s self-blaming, his acknowledgment of his inability to help himself from financial and mental issues, and a constant feeling of being a disappointment to people who had supported him.

Incorporating Global Englishes Varieties into EFL Classrooms: Development of Listening Comprehension and Pronunciation

AbstractWith the rise of different English varieties, scholars argued for reconceptualizing teaching pedagogies to acknowledge and incorporate diverse Global English (GE) varieties into the classroom (Rose & Galloway, 2019). However, very few studies explored the effectiveness of such innovative pedagogy on language skills. This study investigated the effect of an 8‐week GE‐oriented English intervention on the development of learners' listening comprehension and pronunciation. Two intact classes of undergraduate English learners in China were sampled over one semester. The intervention group (n = 29) received (a) awareness‐raising activities about different GE varieties and (b) explicit teaching of and exposure to different spoken varieties of English and their linguistic features. The control group (n = 21) received instruction modeled on American English. The intervention lasted 8 weeks (one session of 45 minutes/week). A pretest, an immediate posttest, and a delayed posttest (3 weeks after) were administered. Results from mixed effect models suggested (a) positive, yet unsustained effects on listening comprehension of GE varieties and (b) no effect on pronunciation development. Findings provide preliminary evidence for the practicality and effectiveness of GE‐oriented language classrooms.

Fine grain emotion analysis in Spanish using linguistic features and transformers

Mental health issues are a global concern, with a particular focus on the rise of depression. Depression affects millions of people worldwide and is a leading cause of suicide, particularly among young people. Recent surveys indicate an increase in cases of depression during the COVID-19 pandemic, which affected approximately 5.4% of the population in Spain in 2020. Social media platforms such as X (formerly Twitter) have become important hubs for health information as more people turn to these platforms to share their struggles and seek emotional support. Researchers have discovered a link between emotions and mental illnesses such as depression. This correlation provides a valuable opportunity for automated analysis of social media data to detect changes in mental health status that might otherwise go unnoticed, thus preventing more serious health consequences. Therefore, this research explores the field of emotion analysis in Spanish towards mental disorders. There are two contributions in this area. On the one hand, the compilation, translation, evaluation and correction of a novel dataset composed of a mixture of other existing datasets in the bibliography. This dataset compares a total of 16 emotions, with an emphasis on negative emotions. On the other hand, the in-depth evaluation of this novel dataset with several state-of-the-art transformers based on encoder-only and encoder-decoder architectures. The analysis compromises monolingual, multilingual and distilled models as well as feature integration techniques. The best results are obtained with the encoder-only MarIA model, with a macro-average F1 score of 60.4771%.

The phonology morphology and syntax of Sundanese

Sundanese is an indigenous language that is spoken in West Java, Indonesia. This study considers the phonology, morphology, and syntax of Sundanese based on a corpus of more than two hundred words. It specifically aims to contribute to the literature of the language by providing some linguistic characteristics of Sundanese and comparing results to what has been previously introduced in the literature. Some previous studies of Sundanese have extensively covered the syntactic and some morphological structures of the language, but only a few studies have covered the phonological aspects. This study aims to provide a comprehensive analysis of the morphological, syntactic, and phonological aspects of Sundanese based on the production of a native speaker of the language. The participant translated all words into Sundanese and provided the singular and the plural forms for each word in the list. The participant also produced sentences using the same words in the corpus. Data were then observed, and patterns were grouped and categorized for analysis. Results showed some similarities and differences to the findings of previous work in the field. Conclusions were drawn and comparisons were made where appropriate.

The Linguistic and Thematic Features of Brand Names in Arabic and English: A Contrastive Study

This study investigates the similarities and differences between brand names in Arabic and English, namely Jordanian and American brands. Brand names are analyzed in terms of their thematic associations and their linguistic features. The focus is on three types of Jordanian and American products: food, clothing and services. The methodology followed is quantitative and qualitative, adopting the framework of Chan & Huang (2001) who classify the brands into different categories based on their thematic associations into seven categories. However, this framework has been adapted to include three more categories. The results reveal that the brand names of both countries have similar thematic associations but with different frequencies. It is found that the frequencies of the thematic categories significantly differ between the two countries (and the two languages) regarding the three investigated types of brands. In relation to their linguistic features, some similarities and differences are reported.

EXPLORING ABBREVIATED RAILWAY TERMINOLOGY IN ENGLISH: A COMPREHENSIVE ANALYSIS

This study investigates the provision and usage of abbreviated terms within railway terminology in the English language. Through a thorough examination of existing literature, an analysis of linguistic features, and a survey of railway industry professionals, this research aims to uncover prevalent abbreviated terms, their formation, and their significance within the railway domain. By shedding light on the use of abbreviations in railway terminology, this study contributes to a deeper understanding of communication practices within the industry.

Bidirectional correlation-driven inter-frame interaction Transformer for referring video object segmentation

Referring video object segmentation (RVOS) aims to segment the target object in a video sequence described by a language expression. Typical multimodal Transformer based RVOS approaches process video sequence in a frame-independent manner to reduce the high computational cost, which however restricts the performance due to the lack of inter-frame interaction for temporal coherence modeling and spatio-temporal representation learning of the referred object. Besides, the absence of sufficient cross-modal interactions results in weak correlation between the visual and linguistic features, which increases the difficulty of decoding the target information and limits the performance of the model. In this paper, we propose a bidirectional correlation-driven inter-frame interaction Transformer, dubbed BIFIT, to address these issues in RVOS. Specifically, we design a lightweight and plug-and-play inter-frame interaction module in the Transformer decoder to efficiently learn the spatio-temporal features of the referred object, so as to decode the object information in the video sequence more precisely and generate more accurate segmentation results. Moreover, a bidirectional multi-level vision-language interaction module is implemented before the multimodal Transformer to enhance the correlation between the linguistic and multi-level visual features, thus facilitating the language queries to decode more precise object information from visual features and ultimately improving the segmentation performance. Extensive experimental results on four benchmarks validate the superiority of our BIFIT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available in https://github.com/LANMNG/BIFIT.

The contribution of written corrective feedback and its association with working memory on the development of EFL learners’ English plurals

Although numerous studies have attested to the effectiveness of written corrective feedback (WCF) in promoting second language (L2) accuracy, the extent to which WCF contributes to acquisition is still debatable. This study thus aimed to investigate the effects of WCF on the development of Thai EFL (English as a foreign language) learners’ implicit and explicit knowledge of English plurals, and the extent to which learner differences in working memory capacity (WMC) moderated the efficacy of WCF. This experimental study adopted a pre–post–delayed-posttest design and was carried out over the course of a 9-month period. Seventy-five low intermediate learners were randomly assigned to the direct focused, direct unfocused and the control group. All groups completed a metalinguistic knowledge, untimed grammatical judgement, timed writing, timed oral elicited imitation, and two working memory tests. Questionnaires and interviews were also administered to explore potential variables that may moderate the effect of WCF. During treatment, participants wrote a timed essay and received feedback. After the 6-week treatment period, the immediate posttest was completed and the delayed posttest was administered three months after the posttest. The results revealed that WCF was effective in assisting learners’ plural acquisition, suggesting that WCF is beneficial especially to the development of linguistic features influenced by learners’ first language (L1). However, WMC was not found to moderate the efficacy of WCF. The explicitness of WCF, instructional context and proficiency level may have contributed to the absence of working memory effect.

Computational model of engagement with stigmatised sentiment: COVID and general vaccine discourse on social media

The growth rate of new social media users continues to surpass new Internet users and new unique mobile phone subscribers and this trend remains consistent over the past 5 years (2019–2023). The most frequently visited types of websites or apps worldwide are chat and messaging, closely followed by social networks and this trend has also remained relatively constant. The dominating role of social media, especially as a source for information seeking, is staggering, particularly during the COVID-19 pandemic. However, the research in Keller et al. (J Mec Internet Res 16:e8, 2014) indicates that not many experts consider social media as a tool for sharing their expertise or for integrating social media into their research efforts. This is a troubling fact, especially considering that stigmatised health narrative are fueled in the face of uncertainty and spread very quickly among the lay population. The latter contributes to the spread of misinformation and, consequently, fosters hesitancy about preventive measures such as vaccines. This research presents new evidence on engagement with stigmatised vaccine discourse on Facebook (Meta), Twitter (X), YouTube and Reddit. Engagement with health-related sentiment can be an important indicator of perceptions regarding preventive measures. The current research can draw the attention of public health experts to the connection between stigmatised discourse and engagement in health discussions, as well as the potential impact of other linguistic features on engagement. It can also guide health authorities and medical professionals in developing effective communication strategies for the general public. Moreover, there are only a handful of studies discussing engagement with socially shared health-care discussions. The research focuses on examining engagement with stigmatised sentiment in vaccination discussions during and prior to the COVID-19 pandemic, using a cross-sectional approach. The study is based on primary data from social media domains, supplemented by secondary data analyses of literature related to the topic. To achieve the research goal, the study employs a multi-method design primarily based on quantitative methods of content analyses, such as Linguistic Inquiry and Word Count (LIWC) (Pennebaker et al. in Linguistic inquiry and word count: Liwc 2001, vol 71. Lawrence Erlbaum Associates, Mahway, 2001) to extract general language features and analyse stigmatised sentiment as the result of previous research findings in Straton et al. (Stigma annotation scheme and stigmatized language detection in health-care discussions on social media. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC, 2020), pp 1178–1190 (European Language Resources Association, 2020), Straton (Appl Intell, pp 1–26, 2022). Unsupervised K-means clustering methods, along with F-score and Z-score measures, are applied to draw insights from the features. The goal is to explore the phenomena of engagement with socially shared health information, investigate whether stigma can affect the engagement, and identify any other language features that may have an impact. Highly engaging general vaccine discussions before the pandemic appear to be more planned and less emotional. They exhibit reasoning and differentiation typical of more complex discussion sections, incorporating references to authority and family concerns simultaneously. The engaging messages convey stigmatised sentiment, likely shared either with the intent to deceive, or are based on a genuinely biased belief. Some elements of stigmatised discussions indicate deception, while others are more frequent in truthful statements. Engaging content in COVID vaccine discussions, similar to general vaccine content, lacks emotional elements and exhibits high linguistic complexity. However, there seems to be no connection between engagement and stigmatised sentiment. This difference is likely due to the removal of public anti-vaccination pages during the pandemic where anti-vaccination groups moved to discussion section of posts that try to disprove vaccine conspiracy theories. Mixing polarised groups will result in different feedback and engagement with the content. Almost half of the features in COVID and general vaccine discussions have a similar impact on engagement. However, further exploration of the findings suggests that there are distinct differences. Understanding the differences is very important to prevent generalisation errors in the conclusions drawn from the data. Engagement with written text on social media should be measured within a narrow scope: limited regional, demographic and temporal scope. Otherwise, interpretation of the findings risks on being inconsistent.

AI-INFUSED CHATBOTS FOR CUSTOMER SUPPORT: A CROSS-COUNTRY EVALUATION OF USER SATISFACTION IN THE USA AND THE UK

As organizations increasingly integrate artificial intelligence (AI)-infused chatbots into their customer support strategies, understanding user satisfaction becomes paramount. This abstract provides an overview of a cross-country evaluation that examines user experiences with AI-driven chatbots for customer support, drawing insights from both the USA and the UK. The evaluation explores the deployment of AI-infused chatbots in customer service scenarios, focusing on how users in these two regions interact with and perceive the effectiveness of chatbot interactions. The study considers the impact of cultural nuances, linguistic variations, and user expectations on the overall satisfaction with AI-driven customer support. In the USA, a market known for its technological adoption and diverse consumer landscape, the evaluation investigates how AI-infused chatbots contribute to enhancing customer experiences. It explores factors such as response time, accuracy, and the adaptability of chatbots in addressing diverse customer queries. In the UK, with its unique cultural and linguistic characteristics, the study examines how AI-driven chatbots align with customer expectations and preferences. It considers the role of chatbots in streamlining customer interactions, providing timely resolutions, and maintaining a high level of user satisfaction. Through a comparative analysis, the evaluation aims to identify patterns, disparities, and commonalities in user satisfaction with AI-infused chatbots in customer support between the USA and the UK. It delves into the challenges faced by chatbots in understanding and responding to regional linguistic nuances, cultural sensitivities, and the adaptability required for diverse customer demographics. The abstract encapsulates the essence of the cross-country evaluation, shedding light on the evolving landscape of AI-driven customer support and its implications for user satisfaction in distinct markets. Insights from this evaluation contribute to the ongoing discourse on optimizing AI-infused chatbot experiences for diverse customer bases, offering valuable considerations for businesses seeking to enhance their customer support strategies on an international scale.. Keywords:  Ai-Infused Chatbots, Customer Support, USA, UK, Cultural Nuances, Technological Landscapes.

"Nolite te bastardes carborundorum": Corpus Stylistics and Language of Margaret Atwood's The Handmaid's Tale

This paper investigates the stylistic and linguistic characteristics of Margaret Atwood's The Handmaid's Tale, a novel of Canadian origin. Employing a corpus-based approach, the researcher aims to analyze its stylistic features in-depth, focusing on specific predetermined categories. Quantitative analysis was facilitated through WordSmith 8.0 software, enabling the identification of keywords in context, which serve as indicators of the linguistic style within the selected corpus. Results indicate that the novel employs simple language and concise sentence structures. Moreover, it demonstrates lexical diversity, encompassing various themes with a higher type-token ratio (TTR), indicative of a diverse vocabulary. Notably, there is a notable prevalence of first-person singular pronouns throughout the text. Regarding content word distribution, verbs are the most frequently utilized, followed by nouns, pronouns, adverbs, and adjectives—additionally, the analysis of keywords aids in uncovering stylistic and linguistic elements embedded within the text.

Linguistic stylistic features of epithets in english and uzbek fiction

The purpose of this study is to identify and analyze the linguistic and stylistic features of epithets to reveal its both differences and types in English and Uzbek fiction. Moreover, this paper aims to analyze various types of an epithet which is one of the most common types of literary devices and is an artistic definition that creates a figurative, vivid picture of a concept or object.

BiLSTM-CNN with fixed weight approach for tracking speech articulatory features

Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications.