Text Corpus Research Articles

Empirical application of sentiment analysis and emotions in Spanish: A post-cognitivist approach

Text mining has led to growth in sentiment analysis (SA) across various research disciplines. The pandemic has provided a unique and special context for analysing students’ written expression. We utilised comments from a survey conducted during the pandemic to create a corpus for SA. The corpus comprises 25,197 words extracted from over 600 comments in Spanish, collected during a survey that lasted around 20 days. We aim to detect sentiments and emotions from this corpus using SA. However, some essential and little-discussed issues in literature should be addressed, such as its relationship with post-cognitivist theory. This paper uses the post-cognitivist approach to analyse emotions and sentiments through SA with the Spanish lexicon in the economics of education. Literature in this area needs further development, especially in Spanish. The article shows that the emotions and sentiments of students in challenging situations can be identified through a corpus of student comments. However, specific elements should be considered while interpreting emotions and sentiments within the framework of post-cognitivism methodologies. Recognising that the human experience is a complex interaction, it is essential to consider the emotional nuances within the context in which they develop. Addressing this issue from the post-cognitivist approach is one of several ways to carry out this task. Using SA and emotions to analyse a text corpus is still helpful for researchers who follow the post-cognitivist approach. However, combining this technique with other qualitative and in-depth computational methods is essential to fully understanding the emotional experiences within their respective contexts.

Defending the “Backward Civilization”: The Resurrection of a Forgotten 17th Century Text in 20th Century Intellectual Discourse on Islam

It is an irony of history that since texts transcend the intentions and purposes of their authors, their meaning and significance are often contested anew as they enter new historical contexts; even historical texts are thus subject to reading and criticism over time. This article discusses the posthumous fate of Henry Stubbe’s own text on Islamic history, The Rise and Progress of Mahometanism—seen by some to represent a Copernican revolution in the study of Islam. The fate of this work is a clear example of the critical contingencies and fluctuating fortunes experienced by a corpus of texts. The continuing existence of a text as an object open to reconfiguration and re-evaluation is termed its after-history (Nachgeschichte) or afterlife (Nachleben). This notion of the afterlife of an object as a period of critical appreciation and political appropriation aptly defines the fate and fame of The Rise and Progress of Mahometanism. The present study seeks to explore the narrative surrounding a publication authored by Stubbe in the 17th century and finally published by Hafiz Mahmud Khan Shairani, with the critical support of Ottoman intellectual Halil Halid Bey, nearly two centuries later. Its objective is to investigate how the life and contributions of a figure whose work has transcended generations was resurrected within the political backdrop of the 20th century, as evidenced in the columns of Ottoman newspapers.

Social representations of health-disease-care, security, and food sovereignty of indigenous students from Insikiran

The social representations of Insikiran Indigenous students regarding health-illness, care, security, insecurity, and food sovereignty were analyzed, aiming for a contextualized understanding of these topics, which are scarcely explored in the literature. This case study employed an exploratory, descriptive, and qualitative approach and was conducted at the Insikiran Institute of Indigenous Higher Education. The methodology included processing the textual corpus formed by the responses of 22 Indigenous students, using SPSS and IRaMuTeQ software. The analyses encompassed descriptive statistics, lexical analysis, descending hierarchical classification, similarity analysis, and word cloud generation. The results highlighted significant thematic diversity, emphasizing the importance of traditional health practices, concerns with chronic diseases, and the relevance of food sovereignty. The social representations were organized into three main thematic groups, demonstrating the intersections between traditional and academic knowledge. This study underscores the importance of adopting interdisciplinary and culturally adapted approaches to promote health and well-being in Indigenous communities.

Wybory translatorskie XIX-wiecznego słownikarza rosyjskiego jako impuls do badań chronologizacyjnych polskiej leksyki zapożyczonej

Fifty-one entries (with the letter M) in the Russian-Polish dictionary of 1877 which were assigned a discussion of the meaning instead of a real translation, e.g. муссон – wjatr periodyczny [a periodic wind], were examined. These were limited to borrowed Russian units which today correspond to Polish borrowed units, e.g. муссон – monsun [monsoon]. The Polish words are the object of the analysis. In order to discover the reason for their omission from the dictionary, evidence of their use in the corpus of historical texts was sought. It was found that 80% of the borrowings (41 units) occurred in texts before 1876, but some of them were not recorded in the dictionaries of the time and perhaps that is why they did not find their way into the translation dictionary. 20% of the material (10 units) is lexis that entered circulation after the dictionary was published.

DAVOMLI HOZIRGI ZAMON GRAMMATIK SHAKLLARI ANNOTATSIYASI

The article provides information on the annotation and modeling of the grammatical forms representing the present continuous tense in the Uzbek language for the corpus. In this case, the existence of different situ- ations in their use in the text, the possibility of modeling these situations were analyzed. In the analysis, statistical indicators are based on the texts of the educational corpus of the Uzbek language (uzschoolcorpora.uz).

A materials terminology knowledge graph automatically constructed from text corpus

A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Challenges and prospects for teaching ocean literacy in Brazilian schools

This study took as a case study the perspective of teachers working in the State of Rio de Janeiro by 53 interviews to investigate the challenges and prospects of including content about the ocean in Brazilian school classrooms. The data were subjected to content analysis and explored quantitatively using Descending Hierarchical Classification (DHC) and Factor Correspondence Analysis (FCA) of word and terms used by teachers in their discourse. The main difficulties reported in including themes related to the ocean and marine environments in classes were the absence of OL in the school curriculum and university education, as well as the limited time to work on these subjects in classes, which is in line with the challenges faced by teachers from other countries. On the other hand, Brazilian teachers cited particular problems of Brazilian formal education, such as the need for professional development, support for an education with an interdisciplinary emphasis, and provision of financial resources for schools. However, DHC analysis also indicates strong connection of the teachers with the ocean due to sea fascination (24% of the answers in the textual corpus), spiritual and emotional connection (26.7%), leisure (22.7%) and professional interest (26.7%). Furthermore, the textual corpus also identified that teachers recognize the importance of teaching marine topics due to: students live in coastal areas (32.2%), the provision of ecosystem services (19.9%), preservation issues (34.2%), and limited student knowledge on these themes (13.7%). These data can help formulate educational policies to mitigate OL teaching barriers in Brazilian schools.

What should we call mental ill health? Historical shifts in the popularity of generic terms

Substantial attention has been paid to the language of mental ill health, but the generic terms used to refer to it–“mental illness”, “psychiatric condition”, “mental health problem” and so forth–have largely escaped empirical scrutiny. We examined changes in the prevalence of alternative terms in two large English language text corpora from 1940 to 2019. Twenty-four terms were studied, compounds of four adjectival expressions (“mental”, “mental health”, “psychiatric”, “psychological”) and six nouns (“condition”, “disease”, “disorder”, “disturbance”, “illness”, “problem”). Terms incorporating “condition”, “disease” and “disturbance” became less popular over time, whereas those involving “psychiatric”, “mental health” and “illness” became more popular. Although there were some trends away from terms with medical connotations and towards more normalizing expressions, “mental illness” consolidated its position as the dominant term over the study period.

An embedded diachronic sense change model with a case study from ancient Greek

Word meanings change over time, and word senses evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as “kosmos” (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. This paper introduces EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. It is shown empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. The challenges of fitting these models are also discussed.

Conceptual Analysis through Corpus Text Patterns: Researching W.E.B. Du Bois’ Concept of Democratic Despotism via Regular Expressions

Conceptual Analysis through Corpus Text Patterns: Researching W.E.B. Du Bois’ Concept of Democratic Despotism via Regular Expressions

Optimizing large language models in digestive disease: strategies and challenges to improve clinical outcomes.

Large Language Models (LLMs) are transformer-based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts and generate context-based responses. The interest in LLMs has not spared digestive disease academics, who have mainly investigated foundational LLM accuracy, which ranges from 25% to 90% and is influenced by the lack of standardized rules to report methodologies and results for LLM-oriented research. In addition, a critical issue is the absence of a universally accepted definition of accuracy, varying from binary to scalar interpretations, often tied to grader expertise without reference to clinical guidelines. We address strategies and challenges to increase accuracy. In particular, LLMs can be infused with domain knowledge using Retrieval Augmented Generation (RAG) or Supervised Fine-Tuning (SFT) with reinforcement learning from human feedback (RLHF). RAG faces challenges with in-context window limits and accurate information retrieval from the provided context. SFT, a deeper adaptation method, is computationally demanding and requires specialized knowledge. LLMs may increase patient quality of care across the field of digestive diseases,where physicians are often engaged in screening, treatment and surveillance for a broad range of pathologies for which in-context learning or SFT with RLHF could improve clinical decision-making and patient outcomes. However, despite their potential, the safe deployment of LLMs in healthcare still needs to overcome hurdles in accuracy, suggesting a need for strategies that integrate human feedback with advanced model training.

ASPECTS OF SPORT METAPHORS TRANSFER

Sports terminology, as a general knowledge that covers our daily lives, is becoming increasingly popular in the world of linguistics. The use of sport metaphors is becoming more and more common - in mass media, in specialized texts, or in colloquial language. The sport metaphor, as a subject of this study, is the relationship of concepts in the field of sport with other conceptual areas. Our attempt to classify such metaphors in accordance with their transfer in various other areas has led to the achievement of a number of objectives: to study the problem in practice, to identify and analyze the most common sports metaphors, to reveal the fundaments on which the relationship between sport and other conceptual areas is based, to divide knowledge, organized around a sports concept that show our understanding of the world through exemplary models. The proposed models are considered as metaphors oriented by the type of sport in two categories – collective and individual sports, together with their subcategories. The studied textual corpus of the sports metaphor allows conclusions about their relationship with and impact on public speech. The increased use of sport metaphors can be explained by the fact that sport is an area that is attractive and emotionally explicit, intriguing the perceptions of the recipient (the mass reader and listener).

Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation.

Since the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications. This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available.

Developing corpus literacy: A perspective of Latgalian language and cultural studies

ABSTRACT Corpus literacy has been described as the ability to use the technology of corpus linguistics to investigate language and enhance the language development of students. In recent decades, text and speech corpora for regional and lesser-used languages have increasingly been created, and they are considered an important resource of authentic texts for language and culture studies and learning. The Modern Latgalian Text Corpus (MuLa 2022) and the Modern Latgalian Speech Corpus (MuLaR) are, in this sense, available for researchers, learners and teachers of Latgalian. Researchers, teachers, and students as potential discoverers of language and culture often lack knowledge, skills and practice to work with language corpora. The purpose of this article is therefore to discuss the opportunities of using Latgalian language corpora in language and culture education. In this vein, we call for the development of corpus literacy of researchers and teachers as part of their professional development. In support of this call, we offer suggestions on how to facilitate students’ cultural awareness and foster their proficiency in the Latgalian language through language corpora. These recommendations aim to enrich both the research and teaching of language (socio-onomastics in a cultural context) and culture (folklore concept) with the help of corpus tools.

The Passive Voice in the Ancient Egyptian Pyramid Texts I

Summary The present article studies the ancient Egyptian Pyramid Texts passive strategy with an added .t or .tj suffix, the so-called T passive. It sets out to be an analysis of its uses and an interpretation of its meaning. A comprehensive list of data is also provided, along with tables analyzing the number of occurrences of certain syntactic structure, and the number of instances of the same verb using other passive strategies. A consideration of the number of occurrences of every passive form and their distinct uses within the different corpora of texts (Sacerdotal and Personal Texts) determines possible grammatical differences between both sources.

„Protestovali před a v budově řeckého ministerstva financí.“ Krátké nahlédnutí do problematiky zeugmatu

In this paper, we focus on one of the deviations from sentence structure, the zeugma, specifically its frequency in different types of texts. Existing linguistic publications treat usually zeugma as an error to be avoided. In addition to defining the term, we also draw attention to ellipsis and condensation, which are related to zeugma. The frequency of its occurrence in texts will be verified by corpus analysis; the corpus used will be the SYN2020 corpus of written Czech, the ORTOFON_v2 corpus of spoken, informal Czech, the ORATOR_v2 corpus of formal texts, and the web corpus, Araneum Bohemicum Maximum. The analyzed examples illustrate that zeugma occurs in different types of texts and, thus, is not bound to one particular mode. The prevalence of zeugma in professional texts suggests that this device does not affect the clarity and precision of expression that are typical for them. This finding could open up a debate as to whether to consider the zeugma as a standard stylistic device by which language users condense their utterances.

In search of Zion: reconsidering the political category of Zionist utopias

ABSTRACT This article focuses on the so-called Zionist utopias in order to reconsider these unprecedented Jewish utopian novels, which arose in the late nineteenth century, from a different perspective to the canonical one that formed within the Zionist historiography of the 1940s. To do so, it examines the literary textual corpus and includes other new utopian sources that have not been considered thus far. It then offers a brief reconstruction of the rediscovery of this utopian imaginary by Zionist scholars. Finally, this article proposes a new historiographical interpretation based on the disjunction between dreaming of Zion and founding Israel in order to deconstruct the underlying Zionist teleological view and thus reconsider these utopias in the long history of Jewish emancipation.

Advancing language models through domain knowledge integration: a comprehensive approach to training, evaluation, and optimization of social scientific neural word embeddings

This article proposes a comprehensive strategy for training, evaluating, and optimizing domain-specific word2vec-based word embeddings, using social science literature as an example. Our primary objectives are: (1) to train the embeddings utilizing a corpus of social science text, (2) to test their performance against domain-unspecific embeddings using our developed intrinsic and extrinsic evaluation strategy, and (3) to enhance their performance even further by using domain knowledge. As an integral part of this approach, we present SociRel-461, a domain-knowledge dictionary designed for the intrinsic evaluation and subsequent refinement of social science word embeddings. Using a dataset of 100,000 full-text scientific articles in sociology, we train multiple vector space models, which we then benchmark against a larger, pre-trained general language embedding model as part of our extrinsic evaluation. Furthermore, we developed a transfer learning multi-label classification task for extrinsic evaluation. Our findings reveal that domain-specific embeddings outperform their domain-unspecific counterparts in both intrinsic and extrinsic evaluations. We also investigated the retrofitting post-processing method to enhance domain-unspecific embeddings with the domain knowledge embedded in SociRel-461. While retrofitting does not enhance our domain-specific vector space models, it significantly improves the performance of the domain-unspecific embeddings. This highlights the potential of retrofitting for the transfer of domain knowledge to domain-unspecific embeddings. Our results emphasize the importance of utilizing domain-specific word embeddings for better performance in domain specific transfer learning tasks, as they outperform conventional embeddings trained on everyday language.

Abusive Language Detection in Khasi Social Media Comments

Abusive Language Detection in Khasi Social Media Comments

Exploring Semanticity for Content and Function Word Distinction in Catalan

In the realm of linguistics, the concept of “semanticity” was recently introduced as a novel measure designed to study linguistic networks. In a given text, semanticity is defined as the ratio of the potential number of meanings associated with a word to the number of different words with which it is linguistically linked. This concept provides a quantitative indicator that reflects a word’s semantic complexity and its role in a language. In this pilot study, we applied the semanticity measure to the Catalan language, aiming to investigate its effectiveness in automatically distinguishing content words from function words. For this purpose, the measure of semanticity has been applied to a large corpus of texts written in Catalan. We show that the semanticity of words allows us to classify the word classes existing in Catalan in a simple way so that both the semantic and syntactic capacity of each word within a language can be integrated under this parameter. By means of this semanticity measure, it has been observed that adverbs behave like function words in Catalan. This approach offers a quantitative and objective tool for researchers and linguists to gain insights into the structure and dynamics of languages, contributing to a deeper understanding of their underlying principles. The application of semanticity to Catalan is a promising pilot study, with potential applications in other languages, which will allow progress to be made in the field of theoretical linguistics and contribute to the development of automated linguistic tools.