Linguistic Complexity Research Articles

Institutional Trading and Managers' Complex Disclosure: Evidence From Conference Calls

ABSTRACTThis study investigates the impact of managerial linguistic disclosures on institutional trading behaviors during earnings conference calls from 2006 to 2016. Utilizing the linguistic Fog decomposition methodology on textual analysis, it analyzes how the linguistic complexity of managerial communication influences institutional ownership changes. Findings indicate that both obfuscation and informativeness of managerial discourse significantly affect institutional trading, with effects varying based on the informativeness of post‐call analyst reports. This impact is more pronounced under conditions of earnings quality uncertainty, suggesting a heightened reliance on managerial cues by institutional investors during such periods. Additionally, the study differentiates responses among investor types: transient investors are more sensitive to linguistic cues, while dedicated and quasi‐indexer investors focus on the informational content of disclosures. These insights enhance understanding of how managerial communication and analyst report informativeness shape institutional trading behaviors through linguistic complexity's influence on institutional ownership.

Examining Teacher and Student Beliefs in Poetry Education: A Systematic Review within TESL Contexts

This systematic review investigates the intersection of teacher and student beliefs in poetry education within TESL (Teaching English as a Second Language) programs. It synthesizes studies examining how these beliefs influence pedagogical practices, student engagement, and overall learning outcomes in poetry instruction. Utilizing the PRISMA methodology, the review identifies and analyzes 32 peer-reviewed articles published between 1982 and 2024. Findings indicate that teachers' beliefs about poetry significantly shape instructional approaches, often impacting students' attitudes toward the genre. Teachers who view poetry as an essential tool for fostering creativity and cultural understanding tend to implement it more actively in their curricula, enhancing student engagement. Conversely, students often perceive poetry as challenging due to its linguistic complexity, leading to anxiety and disengagement. The review also highlights discrepancies between teacher expectations and student experiences, particularly in assessment-driven environments where poetry is seen as secondary to core language skills. The study concludes that a more collaborative approach to poetry education—integrating student perspectives—can foster more positive attitudes and better align teaching practices with student needs. This research contributes to the broader discourse on literature education in TESL, offering insights into how poetry can be effectively integrated into language instruction to improve both educational outcomes and cultural literacy.

Exploring Metalinguistic Awareness in School-Aged Autistic Children: Insights from Grammatical Judgment.

Metalinguistic awareness, the ability to manipulate and reflect upon language, remains largely unexplored in the autistic population. To address this gap, this observational cross-sectional study examines the metalinguistic abilities of school-aged autistic children in comparison to neurotypical peers in a novel tablet-based Grammatical Judgment Task (GJT) of reduced linguistic complexity engaging two kinds of metacognitive resources. Children had to judge non-verbally whether pre-recorded sentences were grammatically correct or not, following the traditional GJT paradigm assessing metamorphosyntactic skills. In addition, sentences with anomalous meaning that were either grammatically correct or grammatically incorrect were introduced to test metasemantic knowledge. Findings reveal no difference in performance between the groups, with participants performing on average above chance level both on the sentences assessing mere metamorphosyntactic skills and on the sentences placing an additional demand on metasemantics. This study shows that autistic individuals are able to mobilize metalinguistic resources when tested via a task of reduced linguistic complexity.

Transfer learning with YOLOV8 for real-time recognition system of American Sign Language Alphabet

Sign language serves as a sophisticated means of communication vital to individuals who are deaf or hard of hearing, relying on hand movements, facial expressions, and body language to convey nuanced meaning. American Sign Language (ASL) exemplifies this linguistic complexity with its distinct grammar and syntax. The advancement of real-time ASL gesture recognition has explored diverse methodologies, including motion sensors and computer vision techniques. This study specifically addresses the recognition of ASL alphabet gestures using computer vision through Mediapipe for hand movement tracking and YOLOv8 for training the deep learning model. The model achieved notable performance metrics: precision of 98%, recall rate of 98%, F1 score of 99%, mean Average Precision (mAP) of 98%, and mAP50-95 of 93%, underscoring its exceptional accuracy and sturdy capabilities.

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.

The impact of visual information in speech perception for individuals with hearing loss: a mini review.

This review examines how visual information enhances speech perception in individuals with hearing loss, focusing on the impact of age, linguistic stimuli, and specific hearing loss factors on the effectiveness of audiovisual (AV) integration. While existing studies offer varied and sometimes conflicting findings regarding the use of visual cues, our analysis shows that these key factors can distinctly shape AV speech perception outcomes. For instance, younger individuals and those who receive early intervention tend to benefit more from visual cues, particularly when linguistic complexity is lower. Additionally, languages with dense phoneme spaces demonstrate a higher dependency on visual information, underscoring the importance of tailoring rehabilitation strategies to specific linguistic contexts. By considering these influences, we highlight areas where understanding is still developing and suggest how personalized rehabilitation strategies and supportive systems could be tailored to better meet individual needs. Furthermore, this review brings attention to important aspects that warrant further investigation, aiming to refine theoretical models and contribute to more effective, customized approaches to hearing rehabilitation.

Reinforcement of low-resource language translation with neural machine translation and backtranslation synergies

<p><span lang="EN-US">This research investigates challenges and advancements in neural machine translation (NMT), specifically targeting English-to-Kannada translation. Emphasizing the scarcity of data and linguistic complexity in low-resource languages (LRL), particularly Kannada, the study underscores the need for specialized techniques. Starting with exploration of Kannada's historical and cultural significance, the paper highlights critical importance of linguistic comprehension. The primary objective is to develop robust NMT models for precise and contextually relevant translations in low-resource scenarios. The novelty of this research lies in its innovative approach to Kannada NMT challenges, incorporating comprehensive examination of historical and cultural context to establish strong linguistic foundation. Motivated by the urgency to address translation needs in LRL, the paper proposes novel strategies, advocating notably for backtranslation to generate synthetic parallel corpora. Rigorous testing, including bilingual evaluation understudy (BLEU) score assessments, evaluates effectiveness of these proposed approaches. Beyond assessing backtranslation, the study explores challenges faced by Kannada NMT in handling dialectical and spelling variations. The research reports substantial 83-percentage-point average increase in BLEU scores, contingent on aligning unique Kannada terms with the same domain as existing occurrences. This study contributes significantly to Kannada natural language processing by offering novel insights into NMT intricacies and providing practical solutions for enhancing translation accuracy in low-resource settings.</span></p>

The dynamics of changes in linguistic complexity and writing scores in timed argumentative writing among beginning-level EFL learners

Abstract This study investigates longitudinal changes in linguistic complexity and holistic scores in EFL timed argumentative writing among beginning-level learners using Complex Dynamic Systems Theory. It also explores relationships among linguistic complexity over time and with writing scores. Over nine months, 42 Japanese EFL learners completed six timed essays. Linguistic complexity was assessed through lexical, phraseological, and syntactic features. Findings indicate linear growth in writing scores, lexical sophistication, and noun phrase (NP) complexity, alongside stable phrasal complexity and non-linear subordination. Interrelated growth patterns included connections between lexical sophistication and phrasal complexity, and phraseological complexity and subordination. Competitive dynamics were observed between lexical diversity and NP complexity, and phraseological complexity and phrasal sophistication. Linear associations emerged between writing scores and lexical sophistication or syntactic complexity, while a non-linear relationship occurred with phraseological sophistication. This study illuminates the intricate dynamics of linguistic complexity and L2 writing scores among beginning-level learners in argumentative writing.

Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood.

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective.

Incorporating Easy Language in the ICF: Benefits, opportunities, and challenges

The World Health Organization's International Classification of Functioning, Disability and Health (ICF), the international standard for the description of functioning and disability, completely ignores a specific form of accessible communication despite its potential to improve functioning and reduce disability: Easy Language, a form of standard language with substantially reduced linguistic complexity. Starting with a proposal for how and where Easy Language could be appropriately integrated into the ICF, the commentary then outlines the various benefits and opportunities for social policy that could be associated with doing so. It also notes several potential challenges involved in this process. If used correctly, the integration of Easy Language into the ICF could prove useful for both professional users of the ICF and Easy Language target groups.

Measuring linguistic complexity in Chinese: An information-theoretic approach

The measurement of linguistic complexity has been a heated topic in linguistics. While previous studies have mainly centered around English and other European languages, Chinese seems neglected to some extent, and the existing Chinese linguistic complexity metrics have room for improvement. Against these backdrops, the present study applied an information-theoretic approach - the Kolmogorov complexity system - to measure Chinese linguistic complexity and examined its reliability and validity. To this end, a corpus of c.a. 60 million characters was compiled, based on which the morphological, syntactical and overall Kolmogorov complexity metrics and 18 other complexity metrics used in previous studies were calculated and subjected to a series of correlation analyses. The results show that the three Kolmogorov complexity metrics are significantly correlated with each other and with most of the previously-used metrics at the character, lexical, syntactic and collocation tiers, indicating that the Kolmogorov complexity metrics are reliable in measuring Chinese. In terms of validity, a comparison between the Kolmogorov complexity of Chinese and nine European languages and a comparison between the Kolmogorov complexity of Chinese L1 and L2 of different proficiencies show that the Kolmogorov complexity precisely and parsimoniously captured the linguistic features of Chinese. The morphological Kolmogorov complexity reflects the richness of Chinese morphemes and the flexibility of word formation, and the syntactic Kolmogorov complexity supports the view that Chinese is a topic-prominent language. This study both extends the generalizability of Kolmogorov complexity and refines the measurement of the Chinese language.

Relative complexity in a model of word difficulty: The role of loanwords in vocabulary size tests

Recent studies have shown that the frequency effect, although long used as a guide to word difficulty, fails to explain all variance in learner word knowledge. As such, a “more than frequency” conclusion has been offered to explain how lexical sophistication accounts for word difficulty. This study presents a multiple regression model of word-learning difficulty from a data set of monolingual Japanese first language (L1) learners. Vocabulary Size Test (VST) scores of 2,999 L1 Japanese university students were converted to logit scores to determine the word-learning difficulty of 80 target words. Five lexical sophistication variables were found to correlate with word-learning difficulty (frequency, cognate status, age of acquisition, prevalence, and polysemy) above a practical significance threshold. These were subsequently entered into a regression model with the logit scores as the dependent variable. The model (R2 = .55) indicates that three lexical sophistication variables significantly predicted VST scores: frequency (ß = -.28, p = .029), cognateness (ß = -.24, p = .005), and prevalence (ß = 0.22, p = .040). Despite suggestions that complexity studies be interpreted considering what is understood about the construct of linguistic complexity, researchers have rarely made explicit the differences between absolute and relative complexity variables. As some variables can be shown to vary in complexity according to the L1 population, these must be considered in discussions of test generalizability. Although frequency will continue to be the primary criterion for the selection of lexical items for teaching and testing, the cognate status of words can be used to predict the potential learning burden of the word more precisely for learners of different L1 backgrounds.

Mapping the associations among task complexity, emotionality, and linguistic complexity in L2 writing

This study explored the relationship between task complexity, textual emotionality, and linguistic complexity in second language (L2) writing. Fifty-eight L2 English learners performed simple and complex versions of an argumentative writing task manipulated along with resource-­directing variables. The essays were first analysed for textual emotionality with sentiment analysis and then examined using a set of lexical and syntactic complexity and cohesiveness indices. Regarding emotionality, the complex task elicited more positive and negative emotions than the simple task. Further, the complex task led to greater lexical complexity and cohesiveness than the simple task, but the two tasks were not statistically different from each other in terms of syntactic complexity features. Varied patterns of correlations were observed between emotionality metrics and indices of different dimensions of linguistic complexity in the two tasks. Our findings focusing on the intersections of cognitive, socio-emotive, and linguistic components suggest that different task complexity manipulations could generate varying cognitive demands, affecting L2 written production. These findings provide theoretical and pedagogical implications for TBLT researchers and L2 writing practitioners.

Exploring the linguistic complexity of third-grade numerical literacy

Reading numbers aloud, a central aspect of numerical literacy, is a challenging skill to acquire, but the origins of this difficulty remain poorly understood. To investigate this matter, we examined the performance of 127 third- and fourth-grade children who read aloud, in Hebrew, numbers with 2–5 digits. We found several key observations. First, we observed a substantial variation among the 3rd graders—7% and 59% errors in the top and bottom deciles, respectively. Second, the task difficulty stemmed from syntactic processing: Most errors were distortions of the number’s syntax, as opposed to digit substitutions or transpositions, and the main factor affecting a specific number’s difficulty was not its magnitude, as is commonly assumed, but rather its syntactic structure. Third, number reading performance was not predicted by a school-like task that assessed syntactic-conceptual knowledge of the decimal system structure, but rather by knowledge of specific syntactic-verbal rules, suggesting that the syntactic-verbal knowledge is separate from the syntactic-conceptual knowledge. Last, there was a double dissociation between 4-digit numbers and 5-digit numbers, which in Hebrew have completely different syntactic structures: Half of the children showed a significant advantage in one number length compared to the other, with equal numbers of children preferring either length. This indicates that the different syntactic-verbal rules are learned relatively independently of each other, with little or no generalization from one rule to another. In light of these findings, we propose that schools should specifically teach number reading, with focus on specific syntactic-verbal rules.

Planning sentences and sentence intonation in Estonian

The notion of advance planning of sentence intonation is grounded in the positive correlation between the sentence-initial intonation peaks and sentence duration. This study examined real-time sentence planning and intonation using visual world speech production. In two eye-tracking experiments, native Estonian speakers described transitive events involving multiple actors. Conceptual complexity of the resulting picture descriptions was manipulated through a pictorial design, while sentence length was controlled for by manipulating specific task characteristics. In Experiment I, conceptual complexity of the picture descriptions varied together with linguistic complexity, while linguistic complexity was held constant in Experiment II. As the conceptual complexity of utterances increased, the duration of naming gazes also increased, indicating less incremental conceptual planning. Notably, while utterance-initial intonation peaks did not correlate with the relative duration of naming gazes, they were influenced by utterance length. These findings highlight advance planning of intonation in Estonian. Furthermore, they suggest that intonation planning depends on linguistic information that is rapidly activated after establishing a comprehensive conceptual framework during earliest stages of preverbal planning.

Exploring the capabilities of ChatGPT in women’s health: obstetrics and gynaecology

Artificial Intelligence (AI) is transforming healthcare, with Large Language Models (LLMs) like ChatGPT offering novel capabilities. This study evaluates ChatGPT’s performance in interpreting and responding to the UK Royal College of Obstetricians and Gynaecologists MRCOG Part One and Two examinations – international benchmarks for assessing knowledge and clinical reasoning in Obstetrics and Gynaecology. We analysed ChatGPT’s domain-specific accuracy, the impact of linguistic complexity, and its self-assessment confidence. A dataset of 1824 MRCOG questions was curated, ensuring minimal prior exposure to ChatGPT. ChatGPT’s responses were compared to known correct answers, and linguistic complexity was assessed using token counts and Type-Token ratios. Confidence scores were assigned by ChatGPT and analysed for self-assessment accuracy. ChatGPT achieved 72.2% accuracy on Part One and 50.4% on Part Two, performing better on Single Best Answer (SBA) than Extended Matching (EMQ) Questions. The findings highlight the potential and significant limitations of ChatGPT in clinical decision-making in women’s health.

예비 초등교사들의 비구조화된 수학 문제 만들기 활동 분석

Objectives The purposes of this study were to analyze degree of structure and complexity of ill-structured mathematical problems made by prospective elementary teachers. Methods In order to do this, 109 prospective elementary teachers participated in ill-structured mathematical problem posing activities, as a result 24 group work papers were collected. The data was analyzed using framework which determines ill-structured problems and problem complexity. Results The results of this study showed that the degree of structure of the problems posed by prospective teachers varied. The prospective teachers posed problems with relatively high ill-structuredness in terms of authenticity, but with low ill-structuredness in terms of complexity and openness. The prospective teachers posed problems that were low of linguistic complexity but high of mathematical complexity. when prospective teachers asked students to solve problems they posed, most of them expected that difficulties would arise during the phase of carrying out the plan. Conclusions The results indicate that prospective elementary teachers need to develop their competence of posing ill-structured mathematical problems, in particular, there is a need to explore the meaning of openness of ill-structured problems. It also suggests that there is also a need to provide prospective teachers opportunities to anticipate and discuss students’ difficulties in ill-structured problem solving.

The effects of task complexity on linguistic complexity and perception in English native speakers’ and L2 speakers’ argumentative writing

This study investigates how task complexity affects native speakers’ and second language (L2) learners’, and experts’ perceptions of task difficulty in writing tasks. It also explores how task complexity influences English native speakers’ and L2 learners’ linguistic complexity in their writing. Ninety participants performed one simple and one complex argumentative writing task manipulated by task complexity (± elements and reasoning demands) and counterbalanced. Their essays were analyzed using linguistic complexity metrics. The results showed that English native speakers, L2 learners, and experts perceived the complex task as requiring more mental effort, being more stressful, and creating more planning and writing time pressure than the simple task. The native speakers demonstrated greater overall linguistic complexity than the L2 learners in the two writing tasks. However, both English native speakers and L2 learners produced longer T-units and more diverse vocabulary in the complex task than in the simple task but also produced more syntactically similar structures in the complex task. Significant interaction effects of group (L1 and L2) and task (± elements and reasoning demands) were found for five linguistic complexity metrics. Theoretical and methodological implications are discussed in light of Robinson’s cognition hypothesis and Hayes’s writing model.

Automatic Speech Recognition of Conversational Speech in Individuals With Disordered Speech.

This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode-specific training on ASR accuracy. Recordings of read and conversational speech from 27 individuals with various speech disorders were analyzed using both (a) one speaker-independent ASR system trained and optimized for typical speech and (b) multiple ASR models that were personalized to the speech of the participants with disordered speech. Word error rates were calculated for each speech model, read versus conversational, and subject. Linear mixed-effects models were used to assess the impact of speech mode and disorder severity on ASR accuracy. We investigated nine variables, classified as technical, linguistic, or speech impairment factors, for their potential influence on the performance gap. We found a significant performance gap between read and conversational speech in both personalized and unadapted ASR models. Speech impairment severity notably impacted recognition accuracy in unadapted models for both speech modes and in personalized models for read speech. Linguistic attributes of utterances were the most influential on accuracy, though atypical speech characteristics also played a role. Including conversational speech samples in model training notably improved recognition accuracy. We observed a significant performance gap in ASR accuracy between read and conversational speech for individuals with speech disorders. This gap was largely due to the linguistic complexity and unique characteristics of speech disorders in conversational speech. Training personalized ASR models using conversational speech significantly improved recognition accuracy, demonstrating the importance of domain-specific training and highlighting the need for further research into ASR systems capable of handling disordered conversational speech effectively.

The evolution of evolutionary linguistics

Abstract This paper presents a scientometric study of the evolution of evolutionary linguistics, a multidisciplinary field that investigates the origin and evolution of language. We apply network science methods to analyse changes in the connections among core concepts discussed in the Causal Hypotheses in Evolutionary Linguistics Database, a searchable database of causal hypotheses in evolutionary linguistics. Our analysis includes a multipartite network of 416 papers, 742 authors, and 1,786 variables such as ‘population birth rate’ and ‘linguistic complexity’. Our findings indicate a significant increase in the size of concept networks from 1886 to 2022, providing an account of the growth and diversification of evolutionary linguistics as a field. We describe eight major clusters of concepts, and characterize the connections within and between clusters. Finally, we identify hypotheses cutting across clusters of concepts that have a high-betweenness centrality, implying that they might have a higher impact on the field if proven right (or wrong). Furthermore, we discuss the role of databases in cultural evolution and scientometrics, emphasizing the value of interdisciplinary connections and the potential for further cross-disciplinary collaboration in the field of Evolutionary Linguistics.