Linguistic Features Research Articles

A multi-dimensional analysis of corporate blogs

Abstract Using a corpus-based multi-dimensional analysis, this study investigates the linguistic features and variation of corporate blogs from four industries: Service industry, the Wholesale and Retail Trade industry, the Manufacturing industry, and Information industry. The primary goal of this study is to examine the linguistic variation in corporate blogs from different industries. The data used for the current study is a 570,745-word corpus consisting of 995 textual posts from 40 top-ranked corporate blogs. In the multi-dimensional analysis, 67 linguistic features in the corpus are tagged, counted and normalized by using a Multidimensional Analysis Tagger (MAT). We imported the output files containing relevant statistical information from the MAT and then to SPSS 25.0 for a further t-test analysis. Overall, the corporate blogs are closest to the text type of general narrative exposition. The study finds that, as a hybrid genre, corporate blogs are basically informationally dense, non-narrative, interactive, and contextually independent. There are some statistically significant differences between four sub-corpora in corporate blogs. In particular, the Wholesale & Retail Trade sub-corpus is the most distinctive one that is significantly different from others on many dimensions, characterized by giving instructions, or telling stories, rather than persuading their audiences to do something.

L2 interactional competence in a short-term service-learning and study abroad program

Abstract This case study explores one Spanish L2 learner’s interactional competence (IC) during a short-term study abroad program that featured service-learning (SL). The goal of the study is to describe the learner’s IC in interactions in the SL context. Data for analysis derive primarily from dialogues between the focal participant and interlocutors from her SL network and are analyzed qualitatively using Young’s (2019) categories of identity, linguistic, and interactional resources. Key findings include: (a) the participant co-constructed identities as an American and a teacher with her interlocutors; (b) use of linguistic resources suggests accommodation to Peninsular Spanish, possibly heightened by increased contact with native speakers in SL; and (c) SL afforded a space where the learner felt confident trying out linguistic and interactional features. Findings describe the participant’s IC and suggest the value of the SL context for meaningful interaction in short-term study abroad programs.

Less is More: Stress Detection through Condensed Social Media Contents

In the digital age, social media has been a go-to platform for stress-related discussions, yielding valuable data to advance the understanding and detection of stress. Swift identification of stress indicators in these online conversations is essential in enabling immediate support and helping to avert subsequent severe mental and physical health issues, especially during global crises such as pandemics and conflicts. Detecting stress in social media posts automatically poses a formidable challenge. While techniques such as supervised Pretrained Language Models (PLMs) and zero-shot Large Language Models (LLMs) based classifiers have demonstrated significant performance, they exhibit limitations, especially on platforms like Reddit. For example, on Reddit, users tend to write lengthy, expressive posts, which causes these methods to often fail to consider the entire context, leading to incomplete or inaccurate assessments of a user's mental health or stress status. To overcome these limitations, we present a new approach to identifying and classifying stress-related discourse on social media. Our approach involves analyzing condensed versions of user posts, such as user-provided summaries or the "Too Long Didn’t Read" (TLDR) portion of the original post. We question whether these abridged texts can yield a more accurate classification of stress. In this paper, we make the following contributions. First, we investigate the relationship between the performance of the model's perceived textual context and the length of social media posts. Second, we present a novel approach to use the summarized texts for stress detection. We experiment with different classifiers to evaluate their performance on stress detection accuracy using summarized versus full-length posts. Furthermore, by examining the emotional and linguistic features of the original posts and their summaries, we suggest improvements to current state-of-the-art LLM-based stress classifier prompts, thereby enhancing stress detection capabilities. Finally, when user summaries are absent, we synthetically generate meaningful user post summaries by incorporating the power of LLMs. Our results show that the stress detection performance deteriorates for longer posts, and utilizing the TLDR and summaries improves classification outcomes. We also provide augmented datasets containing human and AI-generated summaries for future research in stress detection on social media.

Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features.

Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state-of-the-art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling specific linguistic features indicative of autism. Leveraging ChatGPT's advanced natural language processing capabilities, this research aims to streamline and refine the diagnostic process. Specifically, we compared ChatGPT's performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 13% improvement in both accuracy and F1-score in a zero-shot learning configuration. This marked enhancement highlights the model's potential as a superior tool for neurological diagnostics. Additionally, we identified ten distinct features of autism-associated language disorders that vary significantly across different experimental scenarios. These features, which included echolalia, pronoun reversal, and atypical language usage, were crucial for accurately diagnosing ASD and customizing treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach not only promises greater diagnostic precision but also aligns with the goals of personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions.

Register and the dual nature of functional correspondence: accounting for text-linguistic variation between registers, within registers, and without registers

Abstract During the past 20 years, corpus linguistic research on register variation has yielded important theoretical advances. The first part of this paper discusses these advances and the cumulative body of research that has produced them. In the second part of the paper, we focus on the goals of research on register variation. The traditional goal of the text-linguistic (TxtLx) approach to linguistic variation has been to describe registers and patterns of register variation: describing the linguistic and situational characteristics of registers. In this paper, we explore a related, but distinct, text-linguistic goal: to account for all linguistic variation among texts. Because the TxtLx framework assumes the importance of functional correspondence between linguistic characteristics and situational characteristics, it is reasonable to assume that in addition to register, we can use situational parameters coded continuously at the level of individual texts as additional predictors of text-linguistic variation. We describe the results of an empirical study to show that using both register categories and text-level situational parameters as predictors results in a more comprehensive and explanatory model of text-linguistic variation. In the conclusion we discuss the future of corpus-based register studies, focusing on unanswered questions related to theoretical claims about register.

Toward Application to General Conversation Detection of Dementia Tendency from Conversation Based on Linguistic and Time Features of Speech

Currently, MRI examinations and neuropsychological tests by physicians and clinical psychologists are used to screen for dementia, but they are problematic because they overwhelm medical resources and are highly invasive to patients. If automatic detection of dementia from conversations becomes feasible, it will reduce the burden on medical institutions and realize a less invasive screening method. In this paper, we constructed a machine learning model to identify dementia by extracting linguistic features and time features from the elderly corpus with a control group. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were used in the model. We compared the AUC of the single topic model and the general topic model in three cases: (I) All Features, (II) Gini Impurity, and (III) PCA + Gini Impurity. The AUC of the model constructed using RF in (III) for a single topic was 0.91, showing higher AUC than in the previous study. Furthermore, topic analysis showed that topics with high similarity in utterance content are effective in identifying MCI. In the case of the general topic, the model with AUC of 0.8 showed a high identification performance for unknown topics by cross validation on a topic-by-topic basis, indicating that the general topic model developed in this study can be applied to general conversation.

The role of MOOC forum discussion tasks in learners' cognitive engagement

AbstractBackgroundForums in massive open online courses (MOOCs) enable written exchanges on course content; hence, they can potentially facilitate learners' cognitive engagement. Given the myriad of MOOC forum messages, this engagement is commonly analysed automatically through the linguistic features of the messages. Assessing linguistic features of learners' forum messages involves consideration of the learning tasks. MOOC forum discussion tasks, however, have not been previously considered.Objective and MethodThis study explores the effects of MOOC forum discussion tasks on learners' cognitive engagement. Based on the structure of observed learning outcomes (SOLO) taxonomy, we manually annotate distinct levels of cognitive engagement encouraged in forum discussion tasks and displayed by learners in messages starting discussions (i.e., thread starters). We study the linguistic features of thread starters in relation to the pedagogical design of the discussion tasks. Additionally, we use random‐forest modelling to identify the linguistic and task‐related features that help to categorise learners' cognitive engagement according to SOLO levels.ResultsManual analysis showed that learners' thread starters mainly reflect surface SOLO levels and include few academic words and cohesive language. Random‐forest modelling showed that these linguistic features, together with the SOLO levels encouraged in the discussion tasks, played an important role in identification of learners' cognitive engagement.Major TakeawaysOur results highlight the importance of the pedagogical design of MOOC forum tasks in helping learners engage cognitively. Our study also contributes to the empirical evidence that learners' linguistic choices can afford insights into the quality of their cognitive engagement.

COMPARATIVE ANALYSIS OF THE LINGUISTIC AND INTERACTIONAL FEATURES OF ENGLISH LEARNING WEBSITES

This study conducts a comparative analysis of the linguistic and interactional features of English language learning websites, evaluating their design, implementation, and effectiveness in language acquisition and skill development. The analysis shows that the effectiveness of these websites depends significantly on content accuracy, update frequency, and pedagogical approach. Through evaluating various websites, the study identifies key factors like user engagement and interactivity that contribute to their success. Findings indicate that the most effective websites are those with authentic, regularly updated materials and dynamic, interactive teaching methods. These websites support self-directed learning and meet diverse learner needs. However, many websites lack standardization and pedagogical soundness. The study highlights the necessity for comprehensive evaluations and the need for improved website design to enhance language learning experiences.

A Contrastive Analysis of Linguistic Features of Chinese and American Compliments

Compliments are an integral component of social etiquette and play a significant role in everyday interpersonal communication. This study examines the influence of distinct phonetic characteristics in Chinese and English on the moods and sentiments conveyed through compliments. Through an analysis of compliment strategies and their linguistic functions in common situations, this study compares the differences in vocabulary selection and expression methods between Chinese and American compliments. It also discusses the underlying reasons for these differences from the perspectives of cultural variations, social norms, and thinking patterns. Furthermore, the study analyzes and compares the similarities between Chinese and American compliments in terms of their purposes, topics, and stylistic changes. Overall, despite differences in phonetics, vocabulary selection, and expression methods, Chinese and American compliments exhibit convergence in their macroscopic purposes, themes, and styles due to the constraints imposed by specific conversational scenarios.

A Corpus-Based Critical Discourse Analysis of Chinese and American News Coverage on Climate Change

As the 27th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP27) convened, the climate issue remained a focal point of coverage in both Chinese and American mainstream media. This paper seeks to explore the linguistic features of news discourse of climate change in China and the United States and the discursive strategy applied to strengthen national ideologies. Taking the reports on climate change in China Daily and Los Angeles Times within 2 months after COP27 as the research corpus, this paper examines the language features, the discursive processes of the text and possible social factors through a combination of corpus linguistics(CL) and critical discourse analysis(CDA) under the framework of Fairclough’s Three-Dimensional Model of Discourse. The finding suggests that both countries enhance national ideologies by constructing a responsible national image through objective climate reporting. Chinese media tends to focus on national efforts to improve ecological environment and global cooperation, while US newspaper attaches importance to climate security and pursues a leading position in global ecological governance.

A Contrastive Analysis of English and Chinese Proverbs

This paper will analyze the similarities and differences between English and Chinese proverbs, which have the same origin and linguistic characteristics, both of which come from civil life, myths and legends, literature and foreign languages, with conciseness and vivid image of their characters. But, due to their performances in different geographical, historical, religious and cultural values, they have their own characteristics. However, as the world’s cultural exchanges, the boundaries of countries and nationalities gradually break, English and Chinese proverbs have mutual penetrated and melted with each other. It is based on the full understanding of the importance of proverbs that the paper makes the contrastive analysis of English and Chinese proverbs, through which we can not only understand them better but also get to know them deeper of their differences and similarities.

Acquiring and modeling abstract commonsense knowledge via conceptualization

Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for commonsense reasoning. Despite recent progress in artificial intelligence to acquire and model commonsense attributed to neural language models and commonsense knowledge graphs (CKGs), conceptualization is yet to be introduced thoroughly, making current approaches ineffective to cover knowledge about countless diverse entities and situations in the real world. To address the problem, we thoroughly study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction by acquiring abstract knowledge about events regarding abstract concepts, as well as higher-level triples or inferences upon them. We then apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase. We annotate a dataset on the validity of contextualized conceptualizations from ATOMIC on both event and triple levels, develop a series of heuristic rules based on linguistic features, and train a set of neural models to generate and verify abstract knowledge. Based on these components, a pipeline to acquire abstract knowledge is built. A large abstract CKG upon ATOMIC is then induced, ready to be instantiated to infer about unseen entities or situations. Finally, we empirically show the benefits of augmenting CKGs with abstract knowledge in downstream tasks like commonsense inference and zero-shot commonsense QA.

Investigating the Linguistics Features, Impacts, and Corresponding Strategies of Translation in Feminist Literature

With the growing interest in feminism, feminist translation has become an important field of study. This review provides a comprehensive analysis of feminist translation, focusing on its impact, linguistic characteristics, translation strategies, and applications. It explores how feminist translation critically engages with language to address gender biases and patriarchal norms. The analysis underscores the significance of feminist translation in promoting gender equality and empowering marginalized voices. The discussion on linguistic characteristics highlights how feminist translation scrutinizes gendered language, stereotypes, and linguistic bias. It emphasizes the need to challenge the use of gender-specific terms and advocates for more inclusive language practices. Alternative strategies, such as gender-inclusive language and cultural adaptation, are examined as effective means to challenge linguistic biases and promote equality. Furthermore, the examination of translation strategies delves into the interventions employed by feminist translators. These strategies aim to disrupt patriarchal narratives, expose gender biases, and foster critical dialogue. They include amplifying women's visibility and agency, introducing alternative readings, and incorporating cultural adaptations. The applications of feminist translation in various domains are explored, revealing its impact and transformative potential. Specifically, in literature and arts, feminist translation amplifies marginalized voices, challenges traditional gendered narratives, and facilitates cross-cultural exchange. In legal and political discourse, it addresses gender biases in laws and policy documents. Additionally, feminist translation plays a role in media and activism, disrupting harmful representations and promoting social justice. Feminist translation emerges as a dynamic and transformative practice that confronts gender biases, challenges oppressive discourses, and advocates for gender equality. It recognizes the power of language to shape societal norms and actively employs strategies to subvert gender biases within translation.

Do Gender-Diverse Boards Enhance the Linguistic Features of Corporate Financial Reporting?

SYNOPSIS Textual features, such as readability and disclosure tone, of mandatory financial reports have significant economic consequences. Managers and directors’ demographic attributes can also lead to different reporting styles. This study examines how gender-diverse boards influence the readability and tone of corporate financial disclosures under the framework of upper echelons theory. Using a sample of 3,085 U.S. firm-year observations from 2007 to 2016, we find that gender diversity in the board and audit committee enhances the readability of narrative disclosures and is associated with a less optimistic, litigious, and ambiguous tone in annual reports. This study highlights the contribution of female directors to the quality and transparency of financial disclosures and supports recent regulatory initiatives aimed at enhancing female representation on corporate boards.

Incorporating Fine-Grained Linguistic Features and Explainable AI into Multi-Dimensional Automated Writing Assessment

With the flourishing development of corpus linguistics and technological revolutions in the AI-powered age, automated essay scoring (AES) models have been intensively developed. However, the intricate relationship between linguistic features and different constructs of writing quality has yet to be thoroughly investigated. The present study harnessed computational analytic tools and Principal Component Analysis (PCA) to distill and refine linguistic indicators for model construction. Findings revealed that both micro-features and their combination with aggregated features robustly described writing quality over aggregated features alone. Linear and non-linear models were thus developed to explore the associations between linguistic features and different constructs of writing quality. The non-linear AES model with Random Forest Regression demonstrated superior performance over other benchmark models. Furthermore, SHapley Additive exPlanations (SHAP) was employed to pinpoint the most powerful linguistic features for each rating trait, enhancing the model’s transparency through explainable AI (XAI). These insights hold the potential to substantially facilitate the advancement of multi-dimensional approaches toward writing assessment and instruction.

Research on the Value and Language Features of Chinese Language and Literature Texts Based on Text Mining Technology

The linguistic characteristics of a literary work are the way of thinking embodied in the author’s use of language. From the textual value of Chinese language literature, this paper analyzes the spiritual connotation of Chinese language literature from two dimensions: reading and education. Based on the web crawler technology, we obtain the text data of Chinese language literature from three writers, Bajin, Yu Zheng and Qiong Yao, preprocess the data through data cleaning, Chinese word segmentation, de-duplication, etc., and extract the feature values of the text by using the TF-IDF algorithm. Then the text documents are mapped onto vectors using the VSM model, and the parameters of the LDA topic model are estimated by the Gibbs sampling algorithm in order to better obtain the topic changes of the Chinese language literature texts. This paper carries out linguistic feature verification from the lexical and similarity features of Chinese language literary texts. It is found that the difference in lexical density between Ba Jin’s Cold Night and Resting Garden is only 2.1 percentage points, and the frequency of the verb “to say” is 1,213 times and 735 times respectively. The average sentence lengths of Yu Zheng and Qiong Yao fluctuate within the range of [18.49,34.27], and Qiong Yao’s works have a higher thematic concentration than Zheng Zheng’s works. Analyzing the linguistic features of Chinese language literary texts based on text mining techniques helps to understand the authors’ language usage methods and helps to promote innovative expression paths in literary texts.

The Missionary Voice: Perceptions of an emerging register

In this paper, we report on what we are calling “Missionary Voice,” or a particular way of speaking characteristic to missionaries of the Church of Jesus Christ of Latter-day Saints. The first study elicits perceptions of Missionary Voice by Latter-day Saints in the Intermountain West without reference to any particular recording or person. We find a complex, multifaceted indexical field as well as potential linguistic features, uses for Missionary Voice, and speculative origins. In the second study, we play audio clips and ask listeners to identify the missionaries among them. While people did no better than chance, we zero in on certain speakers and compile a tentative list of acoustic correlates of Missionary Voice. As this is the first study on the language of Latter-day Saint missionaries, we open more questions than we answer, but we hope to show that Missionary Voice is very much a part of Latter-day Saint culture.

Comparative analysis of advocacy strategies in justifications for gender-neutral English

This comparative analysis of arguments for singular they and neopronoun adoption focuses on two justifications: historical presence of gender-neutral language in English and individuals’ freedom to choose affirming terms for themselves. We found significant relationships between support for singular they and historical references, as well as neopronoun support and agency references, indicating a meaningful distinction in the ways language users justify different preferences. The frequency of specific arguments illuminates how people conceptualize language, which arguments are perceived as effective, and who must be convinced, and these findings can inform our understanding of folk perspectives on where and how prestige is granted to linguistic features.

Old Believers in Brazil: Preserving Linguistic Identity

This article presents a study of the history of Russian Old Believers’ emigration to Brazil. As such, it analyses the reasons that allowed them to maintain their linguistic identity, and identifies the features of the dialect of the Russian language of the Old Believers living in Latin America and in Brazil in particular. Old Believers moved to Brazil after centuries of oppression, first leaving Central Russia for the East of the country, Siberia and Primorye, and then, after the 1917 Revolution, many of them moved to Harbin (China). After the 1949 Revolution in China, they turned to the United Nations High Commissioner for Refugees, who sent them to the United States, Canada, Australia and Brazil. Brazil was the first country to grant them visas. The main wave of migration of Old Believers to Brazil took place in 1957–1958, that is, after the policy of nationalization of the New State carried out in 1937–1945 by President Getúlio Vargas, whose goal was to turn all immigrants into Brazilian citizens by banning their native language in both official and everyday communication. Thus, the Old Believers managed to fully preserve their religious, cultural and linguistic identity due to the hermetic nature of their communities and the preservation of their traditional way of life. The dialect of the Old Believers of Brazil retains the typical features of the Nizhny Novgorod dialect of the nineteenth century, in which archaic linguistic features and semantic shifts in the meaning of words were preserved. However, it also contains lexical innovations denoting new concepts of modern life, Spanish and Portuguese borrowings and their adaptation. At the beginning of the twenty-first century, several Old Believer families decided to return to Russia under the State Programme to Assist the Voluntary Resettlement to Russia of Compatriots Living Abroad. Specifically, they returned to Primorye, thus completing their round-the-world trip.

Marathi to Indian Sign Language Machine Translation

Machine translation has been a prominent field of research, contributing significantly to human life enhancement. Sign language machine translation, a subfield, focuses on translating spoken language content into sign language and vice versa, thereby facilitating communication between the normal hearing and hard-of-hearing communities, promoting inclusivity. This study presents the development of a ‘sign language machine translation system’ converting simple Marathi sentences into Indian Sign Language (ISL) glosses and animation. Given the low-resource nature of both languages, a phrase-level rule-based approach was employed for the translation. Initial encoding of translation rules relied on basic linguistic knowledge of Marathi and ISL, with subsequent incorporation of rules to address 'simultaneous morphological' features in ISL. These rules were applied during the ‘generation phase’ of translation to dynamically adjust phonological sign parameters, resulting in improved target sentence fluency. The paper provides a detailed description of the system architecture, translation rules, and comprehensive experimentation. Rigorous evaluation efforts were undertaken, encompassing various linguistic features, and the findings are discussed herein. The web-based version of the system serves as an interpreter for brief communications and can support the teaching and learning of sign language and its grammar in schools for hard-of-hearing students.