Linguistic Features Research Articles

Qualities of Literary Machine Translation: A Corpus-based Case Study

The research assesses and compares the translation performance of two popular machine translation systems, GPT-4o and Youdao AI Translate, in translating into English ten Chinese prose essays excerpted from Selected Modern Chinese Essays 2 by Zhang Peiji. The goal is to discover their linguistic features and investigate how well they can perform in this translation. Through a corpus-based analysis, the research explores the STTR and word/ sentence length of their translations and conducts both automated and human evaluations on their translation quality. It reveals that GPT-4o exhibits higher lexical variety and both the two machine translation systems tend to produce more and shorter sentences than the human translation does. Both of them perform surprisingly well in the translation, as they get relatively high BLUE scores yet low TER scores, as well as high adequacy and fluency rates. Our evaluation results also show that Youdao AI Translate displays generally better performance than GPT-4o in the translation of Chinese-to-English literary texts, and they can complement each other to achieve even better performance, though a certain amount of errors are still present in both of their translations.

Linguistic characteristics of false statements (based on the material of American political discourse)

Linguistic characteristics of false statements (based on the material of American political discourse)

Triangulating text relationship in literary retranslation: The Great Gatsby in Chinese

Abstract In the scenario of literary retranslation, the presence of a previous translation necessarily has influence on retranslators. Systemic analyses of relevant features are needed to reveal the potential influence that an earlier translation exerts on a later one and the strategies the latter takes as a response. Starting with the assumption that a distinctive translation can be identified with a set of consistent features, this study examines the relationship between translations with The Great Gatsby and its nine Chinese versions as a case in point, with a novel method of corpus triangulation. For the discovery of the most effective set of distinctive features, a multi-class Support Vector Machine algorithm is applied on twenty-one linguistic features related to vocabulary, grammar, and textuality for the classification of samples from the nine versions. A principal components analysis then follows to verify the multi-dimensionality of a translation and the intertwinement of its features. Finally, further aided with a sentiment analysis, we examine the faithfulness of translations and their distances from each other. Through cross-validation, this study finds: (1) With the shared source text, different translations show a certain, and in some pairs striking, degree of resemblance both in terms of linguistic features and in the sentiment-based narrative plot; (2) the dependency relation between retranslations and earlier ones is usually manifested in measurable linguistic features; (3) linguistic features of a text are normally combined in a complicated manner and features of different categories inextricably intertwined; (4) the trend of proximity proposed by the Retranslation Hypothesis is discernible in the retranslations; and (5) cross-validation between the examination of linguistic features and sentiment analysis proves to be an effective approach for measuring the distance between translations and the closeness of a translation to the original.

Translanguaging in the Undergraduate Nursing Classroom: An Educational Innovation.

Translanguaging is "the act performed by bilinguals of accessing different linguistic features or various modes of what are described as autonomous languages in order to maximize communicative potential" (Skutnabb-Kangas et al., 2009). Translanguaging can be used as a tool to empower undergraduate nursing students to use their chosen strongest written language for assignments. Students in an undergraduate nursing elective course at a large, public urban university could submit specific noncollaborative (solo) assignments in their language. Three students in a class section of a total of nine students chose to submit one or more assignments in a language other than English. Students reported that this experience was unique and empowering. The instructor noted a difference in the writing level in the language of choice other than English. Nursing educators should consider allowing and/or encouraging students to submit specific written assignments in their chosen language. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Beyond ROUGE: A Comprehensive Evaluation Metric for Abstractive Summarization Leveraging Similarity, Entailment, and Acceptability

A vast amount of textual information on the internet has amplified the importance of text summarization models. Abstractive summarization generates original words and sentences that may not exist in the source document to be summarized. Such abstractive models may suffer from shortcomings such as linguistic acceptability and hallucinations. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) is a metric commonly used to evaluate abstractive summarization models. However, due to its n-gram-based approach, it ignores several critical linguistic aspects. In this work, we propose Similarity, Entailment, and Acceptability Score (SEAScore), an automatic evaluation metric for evaluating abstractive text summarization models using the power of state-of-the-art pre-trained language models. SEAScore comprises three language models (LMs) that extract meaningful linguistic features from candidate and reference summaries and a weighted sum aggregator that computes an evaluation score. Experimental results show that our LM-based SEAScore metric correlates better with human judgment than standard evaluation metrics such as ROUGE-N and BERTScore.

PaSCoNT - Parallel Speech Corpus of Northern-central Thai for automatic speech recognition

This paper proposed a Parallel Speech Corpus of Northern-central Thai (PaSCoNT). The purpose of this research is not only to understand the different linguistic characteristics between Northern and Central Thai, but also to utilize this corpus for automatic speech recognition. The corpus is composed of speech data from dialogues of daily life among northern Thai people. We designed 2,000 Northern Thai sentences covering all phonemes, in collaboration with linguists specialized in the Northern Thai dialect. The samples in this study are 200 Northern Thai dialect speakers who had been living in Chiang Mai province for more than 18 years. The speech was recorded in both open and closed environments. In the speech recording, each speaker must read 100 pairs of Northern-Central Thai sentences to ensure that the speech data comes from the same speaker. In total, 100 h of speech were recorded: 50 h of Northern Thai and 50 h of Central Thai. Overall, PaSCoNT consists of 907,832 words and 6,279 vocabulary items. Statistical analysis of the PaSCoNT corpus revealed that 49.64 % of words in the lexicon belongs to the Northern Thai dialect, 50.36 % from the Central Thai dialect, and 1,621 vocabulary items appeared in both Northern and Central Thai. Statistical analysis is used to examine the difference in speech tempo, i.e. time per phoneme (TTP), syllable per minute (SPM), between Northern and Central Thai. The results revealed that there were statistically significant differences speech tempo between Central and Northern Thai. The TTP speaking and articulation rate of Central Thai is lower than Northern Thai whereas SPM speaking and articulation rate of Central Thai is higher than Northern Thai. The results also showed that the ASR model training using Northern Thai speech corpus provides the lower WER% when testing using Northern Thai testing speech data and provides the higher WER% when testing using Central Thai Testing speech data and vice versa. However, the ASR model training using the PaSCoNT speech corpus provides the lower WER% for both Northern Thai and Central Thai testing speech data.

Fake News Detection Using Machine Learning

The rapid use of social media sites like Facebook and Twitter, along with the advent of the Internet, has allowed for the dissemination of information at a level never before seen... More people than ever before are making and sharing content on social media, and unfortunately, some of it is false or otherwise unfounded. It is difficult to automate the process of determining if a written article contains misinformation or disinformation. Prior to reaching a conclusion on an article's veracity, even a domain expert must consider several factors. Automated news article categorization is our proposed usage of a machine learning ensemble technique in this study. In this study, we examine various linguistic characteristics that can be used to distinguish between real and fake news. Taking use of these features, we evaluate the performance of a variety of machine learning algorithms trained using various ensemble methods on four real-world datasets. Results from experiments show that our suggested ensemble learner method outperforms individual learners. Keywords: World Wide Web, Social Media platforms, Information distribution, Content Sharing Textual Features, Machine Learning, Machine Learning ensemble technique, Real-worlds dataset etc.

Corpus-based Multi-dimensional Analysis of the Register Features of Financial News

Nowadays, people rely heavily on financial news media to get financial updates. But as is well known, for non-native speakers of English, in order to fully understand such news, they also need to learn some register features of such financial news besides adequate English language knowledge. The purpose of this study is to explore and analyze the linguistic features of English financial news, and to help readers effectively understand financial news. Specifically, this study employes multi-feature/multi-dimensional analysis (henceforth MF/MD) method to analyze the register characteristics of English financial news based on the theory of register. Through analyzing the self-compiled corpus of financial news, it is found that that the financial news has four major register features: interactivity, persuasion, information and professionalism. These findings will shed lights on the understanding of financial news as well as the learning and teaching of financial/business English.

Tweeting environmental pollution: Analyzing twitter language to uncover its correlation with county-level obesity rates in the United States

BackgroundEnvironmental pollution has been linked to obesogenic tendencies. Using environmental-related posts from Twitter (now known as X) from U.S. counties, we aim to uncover the association between Twitter linguistic data and U.S. county-level obesity rates. MethodsAnalyzing nearly 300 thousand tweets from January 2020 to December 2020 across 207 U.S. counties, using an innovative Differential Language Analysis technique and drawing county-level obesity data from the 2020 Food Environment Atlas to identify distinct linguistic features in Twitter relating to environmental-related posts correlated with socioeconomic status (SES) index indicators, obesity rates, and obesity rates controlled for SES index indicators. We also employed predictive modeling to estimate Twitter language's predictive capacity for obesity rates. ResultsResults revealed a negative correlation between environmental-related tweets and obesity rates, both before and after adjusting for SES. Contrarily, non-environmental-related tweets showed a positive association with higher county-level obesity rates, indicating that individuals living in counties with lower obesity rates tend to tweet environmental-related language more frequently than those living in counties with higher obesity rates. The findings suggest that linguistic patterns and expressions employed in discussing environmental-related themes on Twitter can offer unique insights into the prevailing cross-sectional patterns of obesity rates. ConclusionsAlthough Twitter users are a subset of the general population, incorporating environmental-related tweets and county-level obesity rates and using a novel language analysis technique make this study unique. Our results indicated that Twitter users engaging in more active dialog about environmental concerns might exhibit healthier lifestyle practices, contributing to reduced obesity rates.

The Menzerath-Altmann Law at the Paragraph Level in Written Chinese: Why Register and Text Size Matter?

ABSTRACT This study explores the hierarchical relationship between paragraphs and sentences in written Chinese texts across 9 registers and 20 text sizes using the Menzerath-Altmann (MA) law. Key findings include: (1) The MA law is reasonably applicable at the supra-sentential level in written Chinese, specifically ‘paragraph-sentence-clause’ under certain boundary conditions. (2) Paragraph-level fittings are influenced by both register and text size. Certain fiction registers even show a decrease in goodness-of-fit as text size increases. (3) Media emerges as a potential factor shaping linguistic feature in literary texts. These findings may shed some light on MA law studies at the supra-sentential level.

Tindak Tutur Ekspresif Keluhan dalam Drama Korea “My Liberation Notes”

This article discusses the complaint speech acts found in the dialogues of “My Liberation Notes” drama. This study aimed to identify strategies and linguistic features of each complaint speech act found in the drama. The collected data were analyzed with qualitative descriptive method using Kim Kyung-Ryung’s theory by classifying data in six complaint speech act strategies that appeared in the dialogues. Each strategy and sub-strategy had different linguistic features such as a variety of the words, grammars, sentence types, expressions, etc. 암시하기(amsihagi)/hints strategy used question form sentence and expression of carefulness, 질책하기 (jilchaekhagi)/rebuking used expression of surprise, 비난하기 (binanhagi)/criticism used form of negation, 짜증나기 (jjajeungnagi)/annoyance used interjection and word with annoying meaning, 내적수식어 (naejeoksusikeo)/internal modification used expression and word that highlight politeness, and 외적수식어 (wejeoksusikeo)/external modification appeared with usage of word and expression that weaken and strengthen the meaning of the action.

Computational analysis of linguistic features in speech samples of first-episode bipolar disorder and psychosis

BackgroundIn recent years, automated analyses using novel NLP methods have been used to investigate language abnormalities in schizophrenia. In contrast, only a few studies used automated language analyses in bipolar disorder. To our knowledge, no previous research compared automated language characteristics of first-episode psychosis (FEP) and bipolar disorder (FEBD) using NLP methods. MethodsOur study included 53 FEP, 40 FEBD and 50 healthy control participants who are native Turkish speakers. Speech samples of the participants in the Thematic Apperception Test (TAT) underwent automated generic and part-of-speech analyses, as well as sentence-level semantic similarity analysis based on SBERT. ResultsBoth FEBD and FEP were associated with the use of shorter sentences and increased sentence-level semantic similarity but less semantic alignment with the TAT pictures. FEP also demonstrated reduced verbosity and syntactic complexity. FEP differed from FEBD in reduced verbosity, decreased first-person singular pronouns, fewer conjunctions, increased semantic similarity as well as shorter sentence and word length. The mean classification accuracy was 82.45 % in FEP vs HC, 71.1 % in FEBD vs HC, and 73 % in FEP vs FEBD. After Bonferroni correction, the severity of negative symptoms in FEP was associated with reduced verbal output and increased 5th percentile of semantic similarity. LimitationsThe main limitation of this study was the cross-sectional nature. ConclusionOur findings demonstrate that both patient groups showed language abnormalities, which were more severe and widespread in FEP compared to FEBD. Our results suggest that NLP methods reveal transdiagnostic linguistic abnormalities in FEP and FEBD.

Bank Emails: The Language of Legit and Scam

Amidst the pandemic, the surge in unsolicited scam emails has made internet technology a significant concern. Distinguishing between scams and authentic/legit emails has become increasingly difficult. This research aimed to identify typical characteristics and language patterns found in emails. It also aimed to identify the common linguistics and technical features of the scams and legitimate bank emails. Two legitimate emails and two scam or fraudulent emails from different banks in the Philippines (i.e., LandBank and BDO). A qualitative method was used to analyze and interpret the language features of the two emails. This led to the identification of shared characteristics found in scams and legitimate emails. A scam email typically includes embedded malicious links, misspellings, formatting issues, suspicious email domains, frequent use of contractions and redundancy, tautology, and imperative mood. On the other hand, legitimate emails exhibit proper punctuation, accurate email domains, appropriate formatting, absence of spelling mistakes, avoidance of contractions and redundancy, and use indicative mood rather than imperative. Scam emails utilize modal verbs to manipulate recipients into divulging personal information. Recognizing these email patterns is crucial for educating users and reducing phishing risks. It is important to note that the interpretation and findings of this study are based on the entire dataset collected. Keywords: Bank Emails, Legit Emails, Linguistic Features, Scam Emails, Phishing

INTERVIEW WITH THE MANAGER: PSYCHOLINGUISTIC FOUNDATIONS OF THE STUDY (Based on the Material of the English Press)

The article presents an analysis of the texts of interviews with managers by using a psycholinguistic ap-proach to identify the characteristic features of the presentation of the manager’s personality in English-language periodicals. Despite the attempts made to study the personality of the manager from various scientifi c perspectives, special psycholinguistic studies of this personality have not yet been conducted. The goal of this article is to identi-fy the individual psychological characteristics of the leader’s personality based on the analysis of psycholinguistic markers in the interview texts. As a material the texts of interviews with managers from English periodicals are used: “The Economist”, “The Enterprise World”, “The Wired”, “Vizaca”, “Mckinsey”, etc. The study showed that interviews with managers diff er in compositional and structural features and are represented by three structural types: structured, semi-structured and non-standardized interview type. These types diff er in the degree of inter-view stereotypeness, the standardization of the journalist’s questions, and their personal focus on a particular inter-locutor. At the fi rst stage of the research the pre-dialog part of the interview is studied since the image of a leader is created by a journalist using certain linguistic means. The main diff erences in the format of headings for interviews with managers were found depending on the structural type of the interview. Linguistic evaluative markers in the texts of interview annotations, characterizing the personality of the leader, are analyzed. Psycholinguistic analysis, deduction and interpretation methods are applied to identify and correlate certain linguistic characteristics with the individual psychological features of the leader’s personality. An initial portrait of a successful English leader is compiled, which is formed through the journalist’s view of his interlocutor and consists of a set of specifi c profes-sional and business qualities: enthusiasm, motivation for success, dynamics, innovativeness, perseverance. Based on the diff erences in the structural organization of the interview, the importance to use qualitative and quantitative methods is justifi ed. The combination of interpretative psycholinguistic analysis with content analysis reveals itself as the most eff ective methodology for further researching the dialogical part of interviews with managers. The re-sults obtained contribute to the practice of journalist’s work, the theory of psycholinguistics and cultural linguistics.

Large Language Model–Based Responses to Patients’ In-Basket Messages

Virtual patient-physician communications have increased since 2020 and negatively impacted primary care physician (PCP) well-being. Generative artificial intelligence (GenAI) drafts of patient messages could potentially reduce health care professional (HCP) workload and improve communication quality, but only if the drafts are considered useful. To assess PCPs' perceptions of GenAI drafts and to examine linguistic characteristics associated with equity and perceived empathy. This cross-sectional quality improvement study tested the hypothesis that PCPs' ratings of GenAI drafts (created using the electronic health record [EHR] standard prompts) would be equivalent to HCP-generated responses on 3 dimensions. The study was conducted at NYU Langone Health using private patient-HCP communications at 3 internal medicine practices piloting GenAI. Randomly assigned patient messages coupled with either an HCP message or the draft GenAI response. PCPs rated responses' information content quality (eg, relevance), using a Likert scale, communication quality (eg, verbosity), using a Likert scale, and whether they would use the draft or start anew (usable vs unusable). Branching logic further probed for empathy, personalization, and professionalism of responses. Computational linguistics methods assessed content differences in HCP vs GenAI responses, focusing on equity and empathy. A total of 16 PCPs (8 [50.0%] female) reviewed 344 messages (175 GenAI drafted; 169 HCP drafted). Both GenAI and HCP responses were rated favorably. GenAI responses were rated higher for communication style than HCP responses (mean [SD], 3.70 [1.15] vs 3.38 [1.20]; P = .01, U = 12 568.5) but were similar to HCPs on information content (mean [SD], 3.53 [1.26] vs 3.41 [1.27]; P = .37; U = 13 981.0) and usable draft proportion (mean [SD], 0.69 [0.48] vs 0.65 [0.47], P = .49, t = -0.6842). Usable GenAI responses were considered more empathetic than usable HCP responses (32 of 86 [37.2%] vs 13 of 79 [16.5%]; difference, 125.5%), possibly attributable to more subjective (mean [SD], 0.54 [0.16] vs 0.31 [0.23]; P < .001; difference, 74.2%) and positive (mean [SD] polarity, 0.21 [0.14] vs 0.13 [0.25]; P = .02; difference, 61.5%) language; they were also numerically longer (mean [SD] word count, 90.5 [32.0] vs 65.4 [62.6]; difference, 38.4%), but the difference was not statistically significant (P = .07) and more linguistically complex (mean [SD] score, 125.2 [47.8] vs 95.4 [58.8]; P = .002; difference, 31.2%). In this cross-sectional study of PCP perceptions of an EHR-integrated GenAI chatbot, GenAI was found to communicate information better and with more empathy than HCPs, highlighting its potential to enhance patient-HCP communication. However, GenAI drafts were less readable than HCPs', a significant concern for patients with low health or English literacy.

Reading Between the Lines: Machine Learning Ensemble and Deep Learning for Implied Threat Detection in Textual Data

With the increase in the generation and spread of textual content on social media, natural language processing (NLP) has become an important area of research for detecting underlying threats, racial abuse, violence, and implied warnings in the content. The subtlety and ambiguity of language make the development of effective models for detecting threats in text a challenging task. This task is further complicated when the threat is not explicitly conveyed. This study focuses on the task of implied threat detection using an explicitly designed machine-generated dataset with both linguistic and lexical features. We evaluated the performance of different machine learning algorithms on these features including Support Vector Machines, Logistic Regression, Naive Bayes, Decision Tree, and K-nearest neighbors. The ensembling approaches of Adaboost, Random Forest, and Gradient Boosting were also explored. Deep learning modeling was performed using Long Short-Term Memory, Deep Neural Networks (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). Based on the evaluation, it was observed that classical and ensemble models overfit while working with linguistic features. The performance of these models improved when working with lexical features. The model based on logistic regression exhibited superior performance with an F1 score of 77.13%. While experimenting with deep learning models, DNN achieved an F1 score of 91.49% while the BiLSTM achieved an F1 score of 91.61% while working with lexical features. The current study provides a baseline for future research in the domain of implied threat detection.

‘What we found is’

Abstract This paper examines two variants of the pseudo-cleft construction which display a WHAT-NP-VP-be pattern with the VP realised with cognitive verbs and the proform do in the context of spoken British English dyadic and multi-party BBC podcasts. It is based on the premise that the construction’s referencing potentials are both cataphoric and projective, and that depending on its contexts, one of the two referencing functions is foregrounded while the other is backgrounded. The analysis focuses on those linguistic features and contextual configurations which either contribute to its cataphoric referencing function, or which go beyond the local cataphoric referencing function and indicate its projective, discourse-organising function. The research is corpus-based and uses quantitative and qualitative methodologies, filtering out the linguistic features and contextual configurations which contribute to assigning the two variants the status of a projective construction with a discourse-organising function. The features under investigation are (1) the semantics of the constitutive NPs and VPs marking for tense, aspect and modality and their uptake in the discourse, (2) degrees of continuity and discontinuity in the cohesive chains triggered by the constitutive parts of the construction. The paper shows that when semantic continuity between the what-clause and what follows is discontinued and thus deferred, the construction’s projective function is foregrounded.

Social media’s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation

Most existing methods for detecting fraudulent news or other forms of disinformation primarily rely on user profiling or content analysis, determining whether a given article aligns with a user’s stylistic or content-related preferences. This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions and the resultant topologies of content propagation trees. In our study, we have introduced a deep learning model based on Graph Convolutional Neural Networks (GCNN) and enhanced with multi-head attention mechanisms. This model leverages a wide range of psycholinguistic attributes extracted from users’ posts, including sentiment, emotional content, linguistic features, personality traits, readability, and communication style. We further enrich these attributes with BERT embeddings to improve textual representation. Our research framework involves creating two distinct graph networks: the user interaction graph and the semantic propagation graph. These graphical representations are essential for visualizing dynamic interactions and patterns of information dissemination among users. For each user, we generate a set of carefully crafted features that capture their unique characteristics and behaviors. These user-specific features are then integrated into the User Interaction Graph, enabling a more nuanced understanding and representation of user interactions within our proposed model. The proposed framework has demonstrated superior performance on benchmark datasets, achieving accuracies and precisions of 91.07% and 91.24% on the FakeNewsNet dataset, and 94.20% and 94.92% on the FibVid dataset, respectively. The results obtained from the experimental evaluation have also revealed the effect of various parameters significant to profile disinformers, signifying a substantial improvement in the field of fake news detection and profiling.

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Linguistic Features of Formal and Informal Letter Writing in Modern English Summary Letter writing is an important communication skill in both personal and professional settings. There are different types of letters, including informal letters to friends and family, and formal letters used in business and academic spheres. The article deals with understanding that the differences between informal and formal letters, the appropriate tone, language, and format are essential for effective letter writing. Formal and informal language serve different purposes. It`s important to know when formal or informal writing is expected as it helps a writer to connect with the reader or listener. Formal and informal English are two distinct styles of language usage that vary in their level of formality, vocabulary, sentence structure and tone. Key words: writing skills, formal letters, informal letters, abbrevations, jargons, audience

An Overlooked Source of Wangerooge Frisian: The Birthday Invitation of Louwine Luths

The paper presents and discusses a hitherto overlooked text in Wangerooge Frisian, a short birthday invitation written in 1934 by the native speaker Louwine Luths. The text was first published in the members’ bulletin of a local genealogical society, which is currently only available in print in a few German archives. In the paper I re-publish the birthday invitation – along with another short fragment of Wangerooge Frisian from the bulletin – and discuss a number of salient linguistic features. The two short texts are of particular interest because they were written down by native speakers of Wangerooge Frisian.