Computational Linguistics Research Articles

Computational hermeneutics of emotion: a comparative study of emotional landscapes in the Dostoevsky’s novel “Crime and Punishment”

This study employs computational hermeneutics to explore emotional representations in Fyodor Dostoevsky’s Crime and Punishment. Using advanced Natural Language Processing (NLP) techniques, including sentiment analysis powered by the BERT model and other tools, we examine the emotional and existential aspects embedded in the narrative. Our approach combines computational tools with traditional hermeneutic methods to deconstruct the emotional journey of the protagonist, Rodion Raskolnikov. We quantitatively analyze his emotional evolution before and after the pivotal crime, mapping his psychological transition from detachment to engagement. The study focuses on four key emotional dimensions: Attitude, Introspection, Sensitivity, and Temper. By transforming qualitative textual data into a quantitative framework, we provide a novel illustration of Raskolnikov’s psychological progression from a state of depersonalization to full emotional contact. This computational analysis is contextualized within a broader philosophical interpretation, drawing on concepts such as Stoic apatheia and Heideggerian Sorge. Our research serves a dual purpose: it enhances our understanding of Dostoevsky’s work while demonstrating the potential of computational methods in literary analysis. By bridging digital humanities and traditional literary criticism, this study contributes to the growing field of digital hermeneutics and opens new avenues for interdisciplinary research in literature, philosophy, and computational linguistics.

Textual Attributes of Corporate Sustainability Reports and ESG Ratings

While the textual attributes of corporate financial documents, such as annual reports, have been extensively analyzed in the academic literature, those of corporate sustainability reports, which serve as a critical channel for nonfinancial disclosure, are relatively under-explored. Given the increasing importance of Environmental, Social, and Governance (ESG) factors in corporate strategy and stakeholder evaluation, understanding the role of textual attributes in sustainability reporting is crucial. This study examines 10,021 hand-collected sustainability reports from Chinese firms between 2009 and 2021, focusing on six key textual attributes: length, readability, tone, boilerplate language, redundancy, and completeness. Using computational linguistics, we analyze how these attributes evolve over time and their impact on ESG ratings provided by both international (MSCI, FTSE) and domestic (SNSI) agencies. Our findings reveal that the length and completeness of sustainability reports significantly influence ESG scores across agencies, demonstrating a shared appreciation for detailed and transparent disclosures. However, international and domestic rating agencies exhibit differing responses to attributes like tone, boilerplate language, and redundancy. These differences highlight variations in evaluation standards, methodologies, and value orientations between global and local stakeholders. The results emphasize the need for firms to tailor their sustainability disclosures to meet diverse stakeholder expectations. This study contributes to the growing body of literature on nonfinancial reporting by providing empirical evidence on how specific textual characteristics of sustainability reports can shape ESG evaluations, offering insights for both corporate communicators and policymakers.

Cognitive Aspects of Translation: The Latest Research in Psycholinguistics and Cognitive Science

The study of the cognitive aspect of translation opens up new possibilities for understanding and analysing the translation process through the prism of cognitive science. By analysing the cognitive processes that take place in the translator's brain while performing translation, it is possible to reveal various aspects of this complex and multifaceted process. The purpose of this article is to conduct a literature review of cognition-oriented translation studies in order to contribute to the development of the science of translation studies. The method included the analysis of related literature from the scientific databases Scopus and Google Scholar. As a result of this study, advanced theoretical models were put forward and developed to reveal the so-called “black box” of the translator, which is considered a key aspect in understanding the cognitive processes of translation. These theoretical approaches aimed to uncover the internal mechanism and strategies used by the translator when performing translation in order to reveal and analyse how these processes occur in the translator's brain. Conclusions – the research focused on translation processes has great potential, as it uses the latest theoretical models and research methods from related cognitive disciplines such as cognitive psychology, cognitive linguistics, neurophysiology and computational linguistics. The paper attempts to identify challenges in cognitive translation research in order to contribute to a broader understanding of the cognitive aspects of translation and to help address key challenges in this area.

Multimodal exploration of the thank God expressive construction and its implications for translation

Abstract Multimodal research in communication and translation studies is increasingly recognized, yet it remains incompletely explored. Leveraging computational linguistics with both Praat for acoustic analysis and the OpenPose and Rapid Annotator tools for visual analysis, this study delves into the intricate dynamics of the expressive construction thank God, providing a comprehensive examination of both visual and acoustic dimensions. Our objective is to uncover nuanced patterns of multimodal communication embedded within this expression and their implications for Translation and Interpreting. Through an analysis of linguistic features and co-speech gestures present in thank God, we aim to deepen our comprehension of how meaning crisscrosses modalities. Our findings underscore the necessity of a multimodal approach in language studies, emphasizing the requisite to preserve emotional and contextual nuances. The analysis unveils the phonological relevance of the duration of the construction’s second vowel, a key factor for translation. Additionally, data reveals a correlation between the emotion of relief and gestures executed with both hands closer to the chest. Overall, these findings contribute to advancing both multimodal communication research and translation studies, shedding light on the role of multimodal analysis in understanding language and translation dynamics, particularly in the context of constructions like thank God.

Ontology extension by online clustering with large language model agents.

An ontology is a structured framework that categorizes entities, concepts, and relationships within a domain to facilitate shared understanding, and it is important in computational linguistics and knowledge representation. In this paper, we propose a novel framework to automatically extend an existing ontology from streaming data in a zero-shot manner. Specifically, the zero-shot ontology extension framework uses online and hierarchical clustering to integrate new knowledge into existing ontologies without substantial annotated data or domain-specific expertise. Focusing on the medical field, this approach leverages Large Language Models (LLMs) for two key tasks: Symptom Typing and Symptom Taxonomy among breast and bladder cancer survivors. Symptom Typing involves identifying and classifying medical symptoms from unstructured online patient forum data, while Symptom Taxonomy organizes and integrates these symptoms into an existing ontology. The combined use of online and hierarchical clustering enables real-time and structured categorization and integration of symptoms. The dual-phase model employs multiple LLMs to ensure accurate classification and seamless integration of new symptoms with minimal human oversight. The paper details the framework's development, experiments, quantitative analyses, and data visualizations, demonstrating its effectiveness in enhancing medical ontologies and advancing knowledge-based systems in healthcare.

Economic aspects of error identification in semi-structured publications in the state language

Due to the rapid growth of information on the Internet and social networks, research in the field of computational linguistics has become very relevant. The volume of information that people and machines create in natural language needs to be processed, analyzed and verified. Information retrieval systems, dialog systems, and machine translation tools are used for this. The range of automatic text processing systems is very wide, it covers various tasks. Finding errors in texts and words, identifying and correcting incorrect words is one of the most important tasks of natural language processing (NLP). The article provides an overview of semi-structured data, methods and technologies for identifying incorrect words in natural languages. The paper gives an overview of semi-structured data, methods and techniques for detecting incorrect words in natural languages. The aim of the research is to develop an effective approach for detecting and correcting errors occurring in Kazakh-language texts, especially in the context of limited resources and unstructured data. The research includes the use of machine learning techniques as well as economic analysis of the costs of developing and implementing such solutions. The proposed approach facilitates the automation of text verification, which can significantly reduce the cost of manual data processing and improve the quality of information in various spheres, including business and public administration.

Editorial: In Celebration of Brian MacWhinney’s Five-Decade Contributions to Psychology, Computational Linguistics, and Modern Languages

It is with great pleasure that we announce this latest edition of this special issue of LTRQ to celebrate the landmark achievements of Professor Brian MacWhinney. Brian MacWhinney is Teresa Heinz Professor of Psychology, Computational Linguistics, and Modern Languages at Carnegie Mellon University. Over a distinguished academic career spanning more than 50 years, Brian has made significant contributions not just to the three areas listed under his professor title, but far beyond.

Today’s Computational Linguistics: Artificial Neural Networks beating the well-known hidden Markov models (HMMs)

Today’s Computational Linguistics: Artificial Neural Networks beating the well-known hidden Markov models (HMMs)

Deep Learning for Stylometry and Authorship Attribution: a Review of Literature

The application of deep learning techniques to stylometry and authorship attribution has emerged as a promising frontier in computational linguistics, offering new possibilities for understanding literary style and authorship in both historical and contemporary contexts. This review paper synthesizes recent advances in the use of deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer architectures, for identifying and attributing authorship based on stylistic analysis. We examine the effectiveness of these models in comparison to traditional statistical methods, highlighting their ability to capture complex linguistic patterns and nuances that are often overlooked by conventional approaches. Furthermore, we explore how deep learning models handle challenges such as multilingual texts, limited data, and variations across genres and periods. This review also addresses the interpretability of neural networks in the context of stylometry and discusses the implications of these methods for fields ranging from literary studies to digital forensics. By providing a comprehensive overview of the current state of research, this paper identifies key trends, challenges, and future directions for the application of deep learning to stylometry and authorship attribution.

Web search results processing in an information support system for decision-making processes

Objectives. The problem of the web search results processing in an information support system for decision[1]making is solved in order to create and correct a meaningful description of a problem situation. An approach to solving this problem is proposed based on the use of thematic text corpora (collections of texts on a specific topic) as knowledge about the subject area, as well as a knowledge representation model based on verbal associations. When solving problems of the web search results processing in a decision-making information support system, five main goals are pursued: the formation of an extended description of a problem situation, the synthesis of a search prescription, an Internet search for information about decisions made, the synthesis of a retelling of the information found, and an assessment of the quality of the found analogues of the decisions made.Methods. Methods of set theory, graph theory and mathematical linguistics are used.Results. A mathematical model has been developed for the web search results processing in an information support system for decision making. The concepts of verbal association of words and texts, as well as pragmatically complete lexical structure, are formalized. The proven properties of such structures provide algorithmization of information processes in the model under consideration.Conclusion. The approach to modeling is based on the formalization of the concepts of the informativeness of words, sentences, texts and the informativeness of verbal associations between them. As an implementation of the model proposed in the article, algorithms have been developed for creating a dictionary of pragmatically complete lexical structures, creating structural-lexical templates for sentences, texts and subject areas, synthesizing a brief retelling of the information found, and assessing the quality of the found analogues of the decisions made.

A survey on pragmatic processing techniques

Pragmatics, situated in the domains of linguistics and computational linguistics, explores the influence of context on language interpretation, extending beyond the literal meaning of expressions. It constitutes a fundamental element for natural language understanding in machine intelligence. With the advancement of large language models, the research focus in natural language processing has predominantly shifted toward high-level task processing, inadvertently downplaying the importance of foundational pragmatic processing tasks. Nevertheless, pragmatics serves as a crucial medium for unraveling human language cognition. The exploration of pragmatic processing stands as a pivotal facet in realizing linguistic intelligence. This survey encompasses important pragmatic processing techniques for subjective and emotive tasks, such as personality recognition, sarcasm detection, metaphor understanding, aspect extraction, and sentiment polarity detection. It spans theoretical research, the forefront of pragmatic processing techniques, and downstream applications, aiming to highlight the significance of these low-level tasks in advancing natural language understanding and linguistic intelligence.

Computer Analysis of Scientific Texts to Identify Ideological Narratives

This article is devoted to the problem of identifying hidden ideological attitudes in scientific articles on economics, as well as defining methods for analyzing the ideological landscape of Russian economic science. The problem of this review article lies in the absence of a developed generally accepted approach to the textual analysis of scientific publications. Thus, there is an extremely wide list of methods that can be used in this case. Nevertheless, the development of the most effective methodology requires a comprehensive analysis of existing approaches individually and a comparison of each of them with the others. The authors associate the solution of the problem of choice in the presence of several approaches and a variety of methods with the definition of such a basis that would allow the formation of a methodology that includes ideological classification within the framework of economic scientific literature, the use of computational linguistics tools, in particular natural language processing, in addition to classical narrative analysis tools to identify "ideological narratives". In this regard, the methods of identifying implicit meanings in economic texts are considered in detail. The proposed tools can be used in further research on this or related topics.

Contextual disparities of “happiness” in Korean and Chinese: A quantitative analysis of “행복” and “幸福”

This study investigates the contextual disparities in the meanings of the Korean Hanja word “행복 [hɛŋbok]” and the Chinese Hanzi word “幸福 [ɕiŋ51 fu35],” both conceptualized similarly to “happiness” in English. Utilizing computational linguistics methodologies, we analyzed 192,249 instances of “행복” from the Modu Corpus and 324,585 instances of “幸福” from the CCL Corpus, employing collocation, bi-gram, and tri-gram techniques. Our analysis reveals that these words embody distinct cultural, social, and political dimensions in their respective contexts. Korean usage of “행복” shows a strong association with community well-being, social infrastructure, and educational development. Frequently co-occurring terms include “가족” (family), “교육” (education), and “사회” (society), reflecting deep-rooted Confucian values. Conversely, Chinese usage of “幸福” emphasizes national prosperity, collective harmony, and individual life quality. Common collocates include “国家” (country), “人民” (people), and “生活” (life), influenced by traditional philosophies and modern political ideologies. The research highlights significant differences in how happiness is conceptualized in relation to government policies. Korean contexts focus on specific quality-of-life improvements, evident in phrases like “행복 주택” (happiness housing) and “행복 도시” (happiness city). Chinese contexts stress the link between national development and individual well-being, as seen in expressions like “人民幸福” (people’s happiness) and “幸福生活” (happy life). These findings underscore the complex interplay between personal fulfillment and social responsibility in East Asian cultures. Our study demonstrates the effectiveness of computational linguistics in analyzing complex cultural concepts, while also revealing limitations in capturing individual subjective experiences. It contributes to contrastive lexicography between Korean and Chinese, offering insights for cross-cultural communication and policymaking in social well-being initiatives.

Gendered Response to Artificial Intelligence (AI) in Modern Linguistics: Evaluating the Perspectives of Senior Lecturers on Technological Innovations

The incorporation of Artificial Intelligence (AI) into contemporary linguistics exhibits a significant and transformational change in the discipline. AI technologies, which include natural language processing (NLP), machine learning, and computational linguistics, have significantly transformed the methods employed by linguists for studying, analyzing, and applying linguistic principles. However, as the integration of artificial intelligence (AI) within modern linguistics has presented novel opportunities, facilitating scholars in their investigation of language at an unprecedented scale and level of intricacy, it is pertinent to understand how language educators; especially, the university lecturers perceive these positive innovations. Nevertheless, the current research is focused on examining the responses of senior lecturers on the integration of AI in modern linguistics. The research objective further centered on gender variation in the responses of these lecturers in regard to technological innovations brought in by the integration of AI in modern linguistics. Using a quantitative research method, a good number of participants who are mainly senior lectures were engaged in an online interview. These participants consisting of forty-six (46) females and thirty-seven (37) males shared their opinions with regard to the focus of the study. Moreover, two important hypotheses were developed for this research and a t-test was conducted to validate these hypotheses. The findings generated from the data analyzed indicated that although there are no significant differences in the perceptions of both male and female lecturers on the integration of AI in modern linguistics, there are some aspects specific to modern linguistics with observable gender variations in responses of the participants. Such aspect includes easy adaption of new AI tools, level of benefits and ethical challenges. Also, while female lecturers address the AI integration in modern linguistics from ethical and beneficial point of view, the male counterparts focused more on accessibility and inclusivity.

HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents.

Sensemaking on a large collection of documents (corpus) is a challenging task often found in fields such as market research, legal studies, intelligence analysis, political science, or computational linguistics. Previous works approach this problem from topic- and entity-based perspectives, but the capability of the underlying NLP model limits their effectiveness. Recent advances in prompting with LLMs present opportunities to enhance such approaches with higher accuracy and customizability. However, poorly designed prompts and visualizations could mislead users into falsely interpreting the visualizations and hinder the system's trustworthiness. In this paper, we address this issue by taking into account the user analysis tasks and visualization goals in the prompt-based data extraction stage, thereby extending the concept of Model Alignment. We present HINTs, a VA system for supporting sensemaking on large collections of documents, combining previous entity-based and topic-based approaches. The visualization pipeline of HINTs consists of three stages. First, entities and topics are extracted from the corpus with prompts. Then, the result is modeled as a hypergraph and hierarchically clustered. Finally, an enhanced space-filling curve layout is applied to visualize the hypergraph for interactive exploration. The system further integrates an LLM-based intelligent chatbot agent in the interface to facilitate the sensemaking of interested documents. To demonstrate the generalizability and effectiveness of the HINTs system, we present two case studies on different domains and a comparative user study. We report our insights on the behavior patterns and challenges when intelligent agents are used to facilitate sensemaking. We find that while intelligent agents can address many challenges in sensemaking, the visual hints that visualizations provide are still necessary. We discuss limitations and future work for combining interactive visualization and LLMs more profoundly to better support corpus analysis.

Inheriting the glory of PāṆinian grammar into computer science

the integration of Pāṇinian grammar principles into computer science represents a significant advancement in computational linguistics and natural language processing. Pāṇini, an ancient Indian grammarian, formulated an elaborate system of grammar known as Aṣṭādhyāyī around the 4th century BCE, which has been revered for its systematic and concise rules governing Sanskrit language structure. In recent years, there has been a growing interest in adapting Pāṇinian principles to enhance computational frameworks for understanding human languages. This paper explores the theoretical foundations of Pāṇinian grammar and its applicability in the context of modern computational linguistics. The integration of Paninian grammar into computer science enhances the accuracy and efficiency of natural language processing system and contributes to preservation and dissemination of ancient linguistic knowledge. Through this the researchers aim to bridge gap between traditional linguistic scholarship and modern computational methodologies. By embracing the systematic and rigorous framework laid out by Pāṇini millennia ago, researchers are poised to achieve new breakthroughs in understanding and processing human language through innovative computational techniques. The journey from ancient Sanskrit grammar to innovative computer science exemplifies a harmonious blend of tradition and innovation in the quest for linguistic knowledge and technological advancement. Keywords - Pāṇini, Aṣṭādhyāyī, Computational linguistics, Machine translation, semantic analysis, syntactic rules.

Parental view of online schools: internet forum analysis

The article reflects on a study of family experiences concerning teaching children in online schools that are eligible to issue state certificates. The topic is relevant due to the digitalization of the educational sphere and the search for new educational strategies for schoolchildren. An analysis of messages on an internet forum of parents that are interested in online schools was carried out. When studying unstructured texts, methods of computational linguistics were used, topic modeling in particular. The authors presented the results of an analysis of nine topics on the Internet forum. The narratives about the diversity of online schools and the quality of education are among them. The participants in the discussion highly appreciate the level of knowledge acquired in online schools but they believe that the intellectual result does depends not on the school but on the motivation of the student and the needs of the family. Informants cite the advantages of online learning as time management, teacher competence, improved academic performance, exam preparation, and increased likelihood of enrolling in a college or university on a budget. Parents consider the disadvantages of online schools to be increased family control of students, excess free time, high cost, and organization of studies. An «ideal online school» requires high-quality educational programs, a comfortable environment, and reasonable prices. According to informants, online education is suitable only for students of middle and high school age, but the array of successful stories of studying in online schools includes stories about primary school students. Popular family strategies are switching to online school in grades 8–9 and enrolling in college; dropping grades 10–11 at the previous school, switching to online learning, and entering university. Among the students, there is a predominance of students who are successful in any educational system and schoolchildren who had difficulty coping with the workload in a traditional school. The findings of the study are useful for social policy in the field of online education and for the development of methods for collecting and analyzing information in sociological research.

Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time

AbstractBased on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, artificial intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using large language models (llms) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical–patient communication using intelligent systems. Consequently, we leverage an llm using the latest natural language processing (nlp) techniques in a chatbot solution to provide interpretable machine learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing nlp-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80% in all evaluation metrics, with a recall value for the mental deterioration class about 85%. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.

Narrative reversals and story success.

Storytelling is a powerful tool that connects us and shapes our understanding of the world. Theories of effective storytelling boast an intellectual history dating back millennia, highlighting the significance of narratives across civilizations. Yet, despite all this theorizing, empirically predicting what makes a story successful has remained elusive. We propose narrative reversals, key turning points in a story, as pivotal facets that predict story success. Drawing on narrative theory, we conceptualize reversals as plot: essential moments that push narratives forward and shape audience reception. Across 30,000 movies, TV shows, novels, and fundraising pitches, we use computational linguistics and trend detection analysis to develop a quantitative method for measuring narrative reversals via shifts in valence. We find that stories with more' and more dramatic, turning points are more successful. Our findings shed light on this age-old art form and provide a practical approach to understanding and predicting the impact of storytelling.

Classifiers of Data Sharing Statements in Clinical Trial Records.

Digital individual participant data (IPD) from clinical trials are increasingly distributed for potential scientific reuse. The identification of available IPD, however, requires interpretations of textual data-sharing statements (DSS) in large databases. Recent advancements in computational linguistics include pre-trained language models that promise to simplify the implementation of effective classifiers based on textual inputs. In a subset of 5,000 textual DSS from ClinicalTrials.gov, we evaluate how well classifiers based on domain-specific pre-trained language models reproduce original availability categories as well as manually annotated labels. Typical metrics indicate that classifiers that predicted manual annotations outperformed those that learned to output the original availability categories. This suggests that the textual DSS descriptions contain applicable information that the availability categories do not, and that such classifiers could thus aid the automatic identification of available IPD in large trial databases.