Linguistic Patterns Research Articles

Using language sample analyses across English dialects: A case-based approach for preschoolers

ABSTRACT This study compared language samples from typically developing 4-year-olds who spoke African American English (AAE), Jamaican English (JE), or Mainstream American English (MAE) to assess the value of using language sample analysis (LSA) measures for characterising language use across dialects of English. Specific LSA metrics included mean length of utterance (MLU) in morphemes and in words, the Index of Productive Syntax (IPSyn), Developmental Sentence Scoring (DSS) and measures of lexical diversity. Children demonstrated diverse linguistic patterns across dialects, but a Kruskal–Wallis H test did not reveal significant differences in scores obtained through LSA measures. Notably, the IPSyn captured morphosyntactic structures in each category across dialects where prior research has highlighted limitations. This preliminary study uses a case-based approach to illustrate the applicability of LSAs in describing linguistic variations across children who speak different dialects of English. Moreover, the findings from this study underscore the potential use of LSAs in describing linguistic patterns to support the characterisation of communication profiles for culturally and linguistically diverse children.

Sydney Speaks corpus: An overview

ABSTRACT Sydney Speaks is a sociolinguistic project exploring variation and change in Australian English. The Sydney Speaks corpus combines two legacy sub-corpora recorded in the 1970s and 1980s (Barbara Horvath’s Sydney Social Dialect Survey and the NSW Bicentennial Oral History Collection) with a contemporary sub-corpus, recorded from 2015 to the present (the ANU Corpus of Sydney Speech). Recordings come from oral histories and sociolinguistic interviews with 265 Sydneysiders, born from 1889 to 2001, stratified according to age, social class, gender and ethnicity. To date, approximately 120 h (1.2 million words) have been transcribed and time aligned at both the utterance and segment level. The corpus makes a unique contribution to the study of ethnic variation: as a longitudinal corpus, it facilitates real-time exploration of ethnic variation, allowing questions around the longevity of ethnolects to be answered; the class stratification of the data allows for consideration of the intersection between class and ethnicity; transcriptions that are time aligned at the level of the Intonation Unit and at the level of the segment allow for analyses of phonetic, morphosyntactic and discourse features; and the content of the interviews both provides contextualization for the linguistic patterns observed and sheds light on Australia’s history.

Enhancing Business Email Writing Ability through the Integration of Genre-based Approach and Data-driven Learning

Email functions as a pivotal tool in facilitating today’s business correspondence. Similar to other written genres, email incorporates distinct conventions and linguistic patterns that are recognized within a particular community. Deviating from the established conventions and recurring patterns can pose challenges for individuals engaging in communication within this genre. This study, which utilized a mixed methods research design, formulated a teaching approach that synergized genre-based approach (GBA) with data-driven learning (DDL) to facilitate business email writing instruction, while its effectiveness in enhancing students’ aptitude for written communication was investigated. The participants comprised 24 EFL university students with elementary (n=12) and intermediate (n=12) English proficiency levels from diverse academic disciplines. They underwent a 10-hour instructional course designed to augment their business email writing competence. Data were collected through pre-test and post-test assessments, supplemented by semi-structured interviews. The findings indicated a statistically significant improvement in participants’ business email writing ability subsequent to the intervention. Qualitatively, students’ post-intervention emails exhibited well-organized structure, comprehensive content, and contextual appropriateness, underpinned by practical, professional, and linguistically accurate language use. Furthermore, students expressed satisfaction with the pedagogical approach as it effectively prepared them for real-world business email communication.

Association of Language Preference with Therapeutic Care for Hospitalized COVID-19 Patients.

There is a paucity of research investigating disparities in utilization of inpatient therapeutics for COVID-19 by language preference. The primary aim of this study was to assess if the likelihood of treatment with novel COVID-19 therapies differed for patients using a language other than English (LOE) relative to English-speaking patients. This was a retrospective observational cohort study of COVID-19 patients hospitalized between March 1, 2020, and June 30, 2022, across 11 hospitals within a single not-for-profit health system. Multivariable relative risks were estimated for the impact of preferred language on the receipt of novel COVID-19 therapies: baricitinib, remdesivir, tocilizumab, and convalescent plasma. This study included 12,510 hospitalized adults with English as the most common preferred language (92.3%) followed by Spanish (3.1%), Somali (1.3%), Russian (0.9%), and Hmong (0.6%). Spanish speakers were more likely to receive any of the novel COVID-19 therapies compared to English speakers (RR 1.45; CI 1.32-1.59). Estimates for Hmong, Somali, Russian, and Other language groups were not statistically significant and closer to the null (aRR range, 0.89-1.12). Linguistic patterns in health outcomes expose inherent heterogeneity within racial and ethnic groups. Our study found that Spanish speakers were nearly 1.5 times more likely to receive any of the four novel inpatient COVID-19 therapeutics in comparison to English speakers. Future research is needed to explore the reasons for the heterogeneous findings including temporal influence, cultural factors, informed consent comprehension, and therapeutic hesitancy in all groups.

A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports With Heuristic Linguistic Patterns

A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports With Heuristic Linguistic Patterns

Reproductive and Adaptive Strategies in Translating Comparative Units (on the Material of the Italian, English and Ukrainian Languages)

The article deals with the problem of rendering comparative units of the Italian language into English and Ukrainian through the lens of the translation strategies of domestication and foreignization. Considering translation as one of the means of intercultural communication, the main research question addresses the effectiveness of the chosen strategies in conveying all the meanings of the original text to the translated language. The study aims to analyze how the above-mentioned strategies influence the perception of comparison and comparative units in various cultural contexts, i.e., in English and Ukrainian translations. The twofold nature of the image underlying the comparative unit might represent certain difficulties due to its national and cultural component or intricate, elaborate associative connections, especially in the case of ad hoc comparisons which are characterized by their novelty and/or unpredictability of associations between the object and its referent. This determines the relevance of the present research. Both translation strategies have their advantages and disadvantages. The data received allow us to talk about the priority of a certain strategy within a certain cultural context / language. English translation tends to reproduce the image of comparative units as accurately and precisely as possible, compensating a certain lack of attention to possible linguistic and cultural patterns and readers’ preferences with accuracy, consistency, and original style, which allow obtaining a holistic and coherent picture in the target language. Whereas the Ukrainian translation gives preference to greater dynamics, using adaptive strategies in rendering images and compensates for certain semantic losses at the expense of «proper», understandable images, which should potentially contribute to effective communication and meet reader’s expectations.

Auto-Diacritization and Stylistic Realization of Arabic Text using Deep Neural Networks

This paper presents QRDiaRec, an advanced diacritization system for Arabic Quranic texts. In Arabic, linguistic style refers to the variations in diacritic markings used to convey different pronunciations, dialects, meanings, and contextual understandings.QRDiaRec addresses the challenge of interpreting Arabic diacritics across multiple linguistic styles, which is crucial for accurate language processing. Unlike traditional systems that generate only one correct form of diacritics, QRDiaRec can recognize and produce multiple valid diacritic forms. This capability is due to its training on a dataset that encompasses seven Quranic linguistic styles.The Qur'an is an ideal case study because it is one text with multiple linguistic patterns, allowing us to recognize different forms of correct diacritization. QRDiaRec employs bidirectional LSTM, GRU, and transformer-based models to convert non-diacritic texts into annotated formats, achieving up to 94.2%accuracy. The system enhances Arabic language processing, impacting NLP, machine translation, and Arabic linguistics.

CORRELATION OF THE TERMS "CLASSIFICATION ATTRIBUTE", "MOTIVATIONAL ATTRIBUTE", "SEMANTIC ATTRIBUTE"

The problem of distinguishing the terms "classification attribute", "motivational attribute", "semantic attribute" arose in connection with the active development of cognitive linguistics and the expansion of its terminology. The article describes for the first time approaches to understanding terms in a comparative aspect. The author analyzes dictionary definitions, consistently identifying classification, motivational, semantic features of lexemes, demonstrating the differences between these features. The author comes to the conclusion that the terms "classification attribute", "motivational attribute", "semantic attribute" differ functionally, their use is due to the paradigm and the field of linguistics in which the researcher works. Purpose. The purpose of the study is to distinguish the terms "classification attribute", "motivational attribute", "semantic attribute". Materials and methods. The material for the study was contexts with these terms extracted from scientific papers on cognitive linguistics, word formation, theoretical semantics, as well as dictionary entries. The descriptive and comparative methods, and method of theoretical analysis, were used. Results. The results of the study showed that the terms "motivational attribute", "semantic attribute", "classification attribute" differ functionally. The study of motivational attribute is aimed at identifying linguistic patterns. The study of semantic attribute is aimed at describing lexical (linguistic) meanings. The analysis of classification attribute aims to identify extralinguistic, namely cognitively significant information about the objects of reality. Practical implications. The results of the study can be used in lexicographic and translation practice, in research on issues of nomination, lexical meaning and linguistic categorization.

Language Choices and Digital Identity of High School Student Text Messages in the New Capital City of Indonesia: Implication for Language Education

This research investigates the language choices and digital identities represented in text messages exchanged by high school students in the New Capital City of Indonesia (Ibu Kota Nusantara/IKN) and the implications of these linguistic practices for language teaching. Using a corpus of messages transmitted via WhatsApp collected from 100 high school students, this study produced 2.1 million tokens of 83,414 word types. This study uses corpus linguistic analysis to investigate the distinctive features of language usage, lexical changes, and communication trends in the digital discourse of IKN high school students. Data analysis utilizes features from AntConc corpus tools, including word lists, collocations, concordances, and N-grams. Concerning the features uncovered the subtle nuances that contribute to the unique linguistic identity of the student authors. This research highlights how digital language patterns attribute individual identity within the context of IKN high school students. The data analysis discovers three linguistic patterns in the electronic texts produced by IKN High School students, reflecting their general digital identity as language users in IKN, namely (1) lexical choice, (2) orthographic selection, and (3) lexical bundles. Furthermore, the study reveals the complex construction of digital identity through language, with students negotiating social positions, connections, and personal identities via their linguistic choices in electronic texts. The research findings contribute to the implications of language teaching in the IKN area and a deeper understanding of how students within the IKN area express themselves through language in the virtual realm, thus shaping their digital identities.

Evaluating Neural Networks’ Ability to Generalize against Adversarial Attacks in Cross-Lingual Settings

Cross-lingual transfer learning using multilingual models has shown promise for improving performance on natural language processing tasks with limited training data. However, translation can introduce superficial patterns that negatively impact model generalization. This paper evaluates two state-of-the-art multilingual models, Cross-Lingual Model-Robustly Optimized BERT Pretraining Approach (XLM-Roberta) and Multilingual Bi-directional Auto-Regressive Transformer (mBART), on the cross-lingual natural language inference (XNLI) natural language inference task using both original and machine-translated evaluation sets. Our analysis demonstrates that translation can facilitate cross-lingual transfer learning, but maintaining linguistic patterns is critical. The results provide insights into the strengths and limitations of state-of-the-art multilingual natural language processing architectures for cross-lingual understanding.

A Stylometric Glance at Novels in Euskara

While Basque has been posited as possibly the oldest existing language on the European continent, it appears in written form only in the sixteenth century. The first Basque novel emerges over 300 years later and to this day the genre lacks exhaustive research. The article sets as its aim a stylometric analysis of selected twentieth- and twenty-first-century Basque novels, sourced from the online platforms Armiarma and Booktegi. These are analyzed based on the frequency of the most frequent words measured using cluster analysis and set against a backdrop of foreign novels translated into Euskara. The results show that the originals in Euskara remain distinct from translated works, pointing to the unique linguistic character of the Basque novel. Some linguistic patterns potentially responsible for this distinction are presented. The results are visualized on a map revealing the chronological evolution and the contribution of the Basque novel to the broader literary landscape.

Корпусная интерпретация англоязычного экологического дискурса: лингвистические закономерности и функциональные особенности

English-language environmental speech has its own linguistic features. Ecological discourse is a relevant, self-sufficient, and highly identical communication continuum. This article introduces a corpus analysis of the English-language environmental discourse with its linguistic patterns and functional interpretation. The texts were considered not as static linguistic objects, but as a system of functional features in speech. The English-language research material was authentic and featured various environmental issues. Corpus analysis is a popular and reliable tool for big linguistic data while the corpus-discursive methodology proved to be an effective interdisciplinary tool. It was applied to the EuroNews Green Corpus, aggregated from 506 English-language texts on environmental issues. This innovative type of corpus can be qualified as instrumentally independent. The research proved the effectiveness of universal case managers or case shells of open architecture, e.g., Sketch Engine. Not only did it manage to collect the data, but also performed quite a complex structure analysis, interpreting, and discourse modeling. Corpus linguistics is looking for qualitatively new opportunities to identify complex linguistic relations. The case study provided a comprehensive description of environmental discourse as an object of linguistic research. Based on statistic details, the authors analyzed the categories of tokens, keywords, and terms to structure them as lemmas, collocations, and n-grams, which were presented as metadata. The metadata modeling provided concordances, thesauruses, and frequency glossaries. Environmental discourse had a pronounced lexical and semantic uniqueness resulting from specific ecological terminology. However, the multidimensional corpus analysis revealed an extremely low frequency of the key concept of ecology. Thus, this corpus study of the English-language ecological discourse demonstrated a good potential for further linguistic research.

Ideologies as conceptual networks: towards a data-intensive approach

ABSTRACT This article introduces a data-intensive approach to visualizing political ideologies as text networks. The methodology combines network theory with collocation analysis from corpus linguistics. Its similarities and differences to Michael Freeden’s morphological approach are highlighted. The prospects are examined by focusing on the case study of Finnish socialism in the early 20th century. Primary sources consist of printed labor newspapers, handwritten newspapers produced by ordinary working people, and parliamentary speeches. The empirical part demonstrates that the network approach is useful for (1) locating key political words based on centrality in the network, (2) identifying clusters of words related to each other more strongly than to the rest of the network, and (3) seeing the structure of an ideological network. The main contribution of the article is methodological for the approach can be applied to other machine-readable sources, both at small and large scales, to automatically extract information on the linguistic patterns characteristic of a political ideology under investigation.

Transitional probability between characters as a component of sentence processing in Chinese

Abstract This study explores the role of transitional probability (TP) in sentence processing in Chinese, a writing system that presents unique challenges due to its character-based structure and lack of word boundaries. The research investigates how the statistical regularities of character meaning, as captured by TP, aid in word segmentation and impact reading comprehension. Utilizing a moving window task, the study examines the processing speed of characters in high versus low TP conditions. Findings reveal that characters in high TP bigram conditions (indicating a consistent semantic association within a bigram) are processed more quickly, underscoring the importance of this statistical property of characters in Chinese sentence reading. These findings challenge conventional notions in Chinese linguistics concerning the relationship between characters, morphemes, and semantics, and suggests an alternative perspective on (and the need for reevaluation of) character-level semantics. The study also highlights the influence of prosodic context on reading speed, indicating that anticipatory linguistic patterns shape reader processing.

Judgment prediction from legal documents using Texas wolf optimization based deep BiLSTM model

The complicated nature of legal texts, a lack of labeled data, concerns about fairness, and difficulties with interpretation represent some of the challenges that judicial judgment prediction models encounter. The approach we propose seeks to conquer these challenges by using advanced techniques for deep learning, such as deep Bidirectional Long Short-Term Memory (BiLSTM) networks to recognize complex linguistic patterns and transfer learning to make more efficient use of data. Employing a deep BiLSTM classifier (TWO-BiLSTM) model based on Texas wolf optimization, the research aims to predict legal judgments. To prepare it for evaluation, it initially collects and preprocesses judicial data. Feature extraction involves statistical and Principal component Analysis (PCA) techniques to generate an extensive feature set. The model undergoes training utilizing these features in addition to preprocessed data. A hybrid Texas wolf optimization tactic, based on the optimization of gray wolves and Harris hawks, is employed to boost performance. The ability of the model to accurately and effectively predict legal judgment has been demonstrated by testing it on different sets of judicial data. The model achieved reasonably well in TP 90, having an accuracy of 97.00%. It also achieved exceedingly well in f-score, precision, and recall, having scores of 97.29, 97.10, and 97.19, correspondingly. The model’s effectiveness was further demonstrated in the k-fold 10 assessment, which exhibited 96.00% accuracy and robustness. In addition, using f-score, precision, and recall metrics of 96.25, 96.89, and 95.96, respectively, the model showed outstanding performance. These outstanding results demonstrate the model’s effectiveness and dependability for providing accurate predictions.

The Unveiling Distress: Harnessing NLP and Deep Learning to Identify Suicidal Signals in Tweets

The rise of social media platforms has provided researchers with unprecedented access to vast amounts of user-generated content, offering a unique opportunity to explore various aspects of human behavior, including mental health. This paper presents a novel approach to identifying suicidal signals in tweets using Natural Language Processing (NLP) techniques and Deep Learning algorithms. We propose a multi-step methodology that involves data collection, preprocessing, feature extraction, and classification. Leveraging state-of-the-art deep learning architectures such as recurrent neural networks (RNNs) and transformer models, our approach aims to accurately detect linguistic patterns indicative of suicidal ideation and distress. We evaluate the effectiveness of our method using a large dataset of annotated tweets and demonstrate promising results in terms of both precision and recall. Furthermore, we discuss the ethical implications and potential applications of our research in suicide prevention and mental health support systems.

Beyond expectations: (Applied) corpus linguistics and a framework for the study of spoken professional talk

The analysis of real-world, recorded, and transcribed texts (i.e., corpora) of professional, spoken communication in the workplace has been conducted quite successfully through corpus-based approaches (Friginal, 2024). Corpus linguistics is primarily a methodological research approach to the study of language, and specifically, discourse structure, patterns, and use (Biber et al., 2010; Thompson & Friginal, 2020), with corpora serving as datasets of systematically collected, naturally-occurring registers of texts utilized for a variety of purposes. The use of corpora has become a popular approach in the quantitative analysis of the linguistic characteristics of written and spoken language, in general, and sub-registers such as oral communication in the workplace, in particular (Egbert et al., 2022; Staples, 2015). Various findings have pedagogical and, more importantly, language policy applications. This paper focuses on the important contributions of an iterative corpus-based framework to examine linguistic patterning in telephone/telephony-mediated professional discourses so as to obtain novel understandings of how talk is used and construed in these domains. Current limitations, emerging contributions from generative AI applications, and a call to action proposing training and assessment models will be discussed.

PRESERVATION OF THE LINGUOCULTURAL CODE IN TRANSLATION OF MEMOIRS

This article explores linguistic and cultural codes as comprising symbolic meanings and procedural schemes that shape understanding and expression. These codes include fixed and variable linguistic units, forming structural and semantic patterns reflecting personal thinking. They underpin artistic and literary expressions, modeling cognitive, semiotic, and linguistic perspectives. The study discusses N. I. Zhinkina’s “subject-schematic” code, transforming thoughts into verbal expressions, and Ch. Fillmore’s frame theory, linking words to knowledge structures. It emphasizes the development of conceptual and mental spaces through language and culture, highlighting their roles in organizing knowledge and facilitating communication.

Success Syntax: A Study of Linguistic Patterns of “Think and Grow Rich”

Success Syntax: A Study of Linguistic Patterns of “Think and Grow Rich”

VERBAL AGGRESSION AND FALSE ARGUMENTS IN CRISIS POLITICAL COMMUNICATION

In this study we focus on political discourse and political communication in periods of crisis in order to characterize the speech of public figures from Bulgarian and Czech political life. The text examines political discourse in Bulgaria and the Czech Republic in the context of important crisis events in the political life of both countries - votes of no confidence in the governments. In the studied linguistic material analogical patterns of realization of communicative strategies are observed: linguistic aggression, use of false argumentation, expressed in accusing the opponent of incompetence and committing fraud, excessive use of lexemes with negative connotation, suggesting a sense of catastrophe and misfortune. The intensification of the message is also achieved through the use of a number of lexemes with a negative connotation, which is characteristic of aggression in political speech. As a means to achieve these goals, communication in political discourse usually uses expressive or other marked lexical expressions, as well as false argumentative strategies, aimed not at the content and nature of the problem, but at attacking the positive image of the communication partner. In the corpus of linguistic material studied, linguistic aggression based on the assertion of one's own personality, independently of the communication partner, was identified with high frequency. In the examples given, there is always a conscious linguistic strategy and intent that is clearly identifiable in a given communicative moment.