Text Corpus Research Articles

From HIV to Mpox: Socio-discursive Analysis of Communicable Diseases, Stigma, and Pathologization in Sexual Minorities in Brazil.

Discrimination and stigma are significant barriers to healthcare for the LGBTQIAPN+ community, necessitating a deeper analysis of their sociocultural causes. There is a notable gap in the literature regarding the understanding of socio-discursive representations and their impact on the stigmatization and pathologization of sexual minorities in the context of communicable diseases. This study aims to discuss the understanding of the sociodiscursive aspects of the health-disease process, particularly in stigmatized infectious diseases affecting the LGBTQIAPN+ community. The focus is on examining how news articles, or the set of analyzed texts (corpus), shape these perceptions. We conducted documentary research with a qualitative and discursive approach using news articles retrieved from Google NewsⓇ about diseases affecting the LGBTQIAPN+ population from 2011 to 2022. The analysis was based on critical discourse analysis, processed using MAXQDA and IRAMUTEQ software. The identified representations predominantly align with biomedical ideology, manifesting in a discourse that normalizes and medicalizes (normative-curative discourse), and notable for its pathologizing and stigmatizing nature. Six classes were found: Ethical professional dilemmas facing stigma, infection and contamination of the LGBT+ population, prejudice and discrimination in the form of information, stigma related to sexual behavior/orientation, Vulnerability and stigma related to infectious diseases, and strategies for minimizing health risk/stigma for the LGBT+ public. The most relevant analytical categories were related to infectious diseases and sexual identity. These themes were identified, indicating that media representations reinforce stigma and maintain unequal health practices (verticalization) for the LGBT+ community. Understanding these patterns within a broader historical context is crucial for promoting health education and strategies that challenge internalized prejudice. The need to reformulate cultural norms and develop health information and education policies is urgent. These policies should be led by professionals with a comprehensive and humanized vision, addressing the diverse needs of the LGBT+ population.

Nepali Text-to-Speech Synthesis Using Tacotron2 and WaveGlow

This research paper presents the development of a Nepali Text-to-Speech (TTS) system under low-resource conditions by adapting pre-trained English Tacotron2 and WaveGlow models. Tacotron2 has been utilized for spectrogram generation, and WaveGlow has been employed for vocoding, with recognition of the pivotal role played by these components in determining the efficacy of a Text-to-Speech (TTS) system. Our approach entails the adaptation of a pre-trained English Tacotron2 model and WaveGlow architecture to Nepali, leveraging limited data resources to craft a Nepali TTS system capable of producing natural-sounding output under low-resource conditions. Through fine-tuning with a Nepali text corpus aligned with its corresponding audio dataset, the pre-trained Tacotron2 model is optimized for spectrogram generation. Subsequently, WaveGlow, our chosen audio synthesis model, is utilized to convert the spectrogram representations into audible waveforms. It is worth noting that our model exhibits limitations in synthesizing audio for a restricted subset of Nepali texts, attributed to challenges stemming from text cleaning and normalization inadequacies.

An updated account of master’s level business school writing: Revisiting the genre family framework

Existing studies of student written genres across business school disciplines were undertaken more than a decade ago. This study has produced an updated overview of master’s level written coursework assignments from core modules across eleven business school degree programmes. Nesi and Gardner’s (2012) genre family framework was applied in analysing task briefs and related documentation to identify social purposes and genres. Results suggest that in the decade since the British academic written English (BAWE) corpus texts were collected, there has been a considerable increase in the proportion of genres with the ‘preparing for professional practice’ social purpose (Nesi & Garner, 2012, p. 36), alongside a considerable drop in proportions of ‘demonstrating powers of informed and independent reasoning’ (Nesi & Garner, 2012, p. 36) genres, which had previously dominated. We argue for the usefulness of further applications of the approach taken in this study across different disciplines and institutional contexts to regularly update knowledge and inform EAP practice. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licenses/by-nc-nd/4.0/ .

Approaches to improve preprocessing for Latent Dirichlet Allocation topic modeling

As a part of natural language processing (NLP), the intent of topic modeling is to identify topics in textual corpora with limited human input. Current topic modeling techniques, like Latent Dirichlet Allocation (LDA), are limited in the pre-processing steps and currently require human judgement, increasing analysis time and opportunities for error. The purpose of this research is to allay some of those limitations by introducing new approaches to improve coherence without adding computational complexity and provide an objective method for determining the number of topics within a corpus. First, we identify a requirement for a more robust stop words list and introduce a new dimensionality-reduction heuristic that exploits the number of words within a document to infer importance to word choice. Second, we develop an eigenvalue technique to determine the number of topics within a corpus. Third, we combine all of these techniques into the Zimm Approach, which produces higher quality results than LDA in determining the number of topics within a corpus. The Zimm Approach, when tested against various subsets of the 20newsgroup dataset, produced the correct number of topics in 7 of 9 subsets vs. 0 of 9 using highest coherence value produced by LDA.

ANALYSIS OF GOOGLE TRANSLATE’S TRANSLATION AND THE AUTHOR’S FROM ALBANIAN TO SERBIAN LANGUAGE

Literary translation, as one of the most complex forms of translation, requires a special approach and attention, given that it involves not only conveying meanings but also preserving the style of the original text. Although the translation of texts using various online tools is becoming increasingly common today, the question arises - are these tools advanced enough, and can they replace the translation done by human authors? As our corpus material, we used specific excerpts from the first chapter of Ismail Kadare’s novel ‘The Palace of Dreams,’ which we translated using Google Translate and compared with the Serbian translation of this novel by Shkëlzen Maliqi. The aim of the paper is to examine the reliability of machine translation, i.e. analysing the mistakes it makes, compared to the translation of competent and professional translators. The comparison process is based on different linguistic disciplines, and we have come to the conclusions that Google Translate has not yet improved enough to become the main tool in the translation of literary texts, where professional translators still play a key role. Of course, the question arises - whether it would perform better in translating other types of literary texts or in translating different language groups, considering its diverse corpus of literary texts in its memory?

Environmental Topics in Contemporary Croatian Dystopian Proseand Prose with Dystopian Elements

Important ecological topics: climate change, pollution of air, water and soil, extinction of plant and animal species and food as a basic human need are in the corpus of dystopian texts and those with dystopian elements. The dystopian component in all of them belongs to the second wave of dystopian fiction (Matus 2009: 7). The corpus consists of a novella “White Promenade” (“Bijela promenada”, 2016) by Ed Barol and eight novels: Battlefield Istria (Bojno polje Istra, 2007) by Danilo Brozović, Centimeter from the Happiness (Centimetar od sreće, 2008) by Marinko Koščec, 2084: House of Great Misery (2084: Kuća Velikog Jada, 2012) by Ivo Balenović, Planet Friedman (2012) by Josip Mlakić, Romeo at the End of the History (Romeo na kraju povijesti, 2015) by Aljoša Babić, War for the Fifth Taste (Rat za peti okus, 2015) by Veljko Barbieri, Dedivination (Dedivinacija, 2018) by Jelena Hrvoj, and Crab’s Children (Rakova djeca, 2019) by Dalibor Perković. The theoretical perspective in the paper is ecocritical: aspects of the relationship between man and the nature that surrounds him are investigated, i.e. to what extent man perceives nature and its inhabitants as equal to himself. The last chapter gives an insight into the results and solutions that some authors offer as a way out of cataclysmic situations, and the meaning and efficiency of these solutions are valued from an ecocritical point of view.

«Oda quiere dezir ffin e acabaminto de las cosas». El origen y la evolución de la expresión «quiere decir» en español

The objective of the paper is to analyze the origin and the evolution of the explanatory value of the construction <querer ‘want’ + infinitive>, documented in Castilian texts from the Middle Ages. Through a relatively large corpus of Latin texts (5th–12th centuries) and Spanish texts (13th–15th centuries), the Author explores the main linguistic and discursive reasons that determined its use in the Spanish language.

Charting the Universe of Metal Music Lyrics and Analyzing Their Relation to Perceived Audio Hardness

We analyze the relationship between the musical and the lyrical content of metal music by combining automated audio feature extraction and quantitative text analysis on a corpus of 124,288 song lyrics from this genre. Based on this text corpus, a topic model is first constructed using Latent Dirichlet Allocation (LDA). For a subsample of 503 songs, scores for predicting perceived musical hardness (heaviness) are extracted using an audio feature model and corroborated via listening tests. By combining both audio feature and text analysis, we (1) offer a comprehensive overview of the lyrical topics present within the metal genre and (2) are able to establish whether levels of hardness are associated with the occurrence of particularly harsh (and other) textual topics. Twenty typical topics are identified and projected into a topic space using multidimensional scaling (MDS). Positive correlations are found between musical hardness and textual topics dealing with “brutal death,” “dystopia,” “archaisms and occultism,” “religion and satanism,” “battle,” and “(psychological) madness,” while there is a negative association with topics like “personal life” and “love and romance.” Further, we show different prevalences of the identified topics within different metal subgenres. In summary, this article presents the first comprehensive computational corpus study of musical and lyrical content in metal music.

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Objectives: Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety event data into meaningful summaries similar to humans. Methods: Microwave ablation safety data from January 1, 2011 to October 31, 2023 were collected and type of failure was categorized by human readers. Using GPT-4 and iterative prompt development, the data were classified. Iterative summarization of the reports was performed using GPT-4 to generate a final summary of the large text corpus. Results: Training (n = 25), validation (n = 639), and test (n = 79) data were split to reflect real-world deployment of an LLM for this task. GPT-4 demonstrated high accuracy in the multiclass classification problem of microwave ablation device data (accuracy [95% CI]: training data 96.0% [79.7, 99.9], validation 86.4% [83.5, 89.0], test 87.3% [78.0, 93.8]). The text content was distilled through GPT-4 and iterative summarization prompts. A final summary was created which reflected the clinically relevant insights from the microwave ablation data relative to human interpretation but had inaccurate event class counts. Conclusion: The LLM emulated the human analysis, suggesting feasibility of using LLMs to process large volumes of IR safety data as a tool for clinicians. It accurately labelled microwave ablation device event data by type of malfunction through few-shot learning. Content distillation was used to analyze a large text corpus (>650 reports) and generate an insightful summary which was like the human interpretation.

Indonesian disaster named entity recognition from multi source information using bidirectional LSTM (BiLSTM)

Precise logistic support is essential after a disaster occurs. It must be timely, accurate, targeted, and based on existing needs. However, obtaining sufficient and accurate information related to logistic distribution locations remains a key problem. Therefore, implementing Named Entity Recognition (NER) can address this issue. In recent years, news coverage through Indonesian digital news media and social media accounts has emerged as a promising source for building a disaster data corpus. This study implemented NER to extract and identify named entities from text-based information, particularly from Indonesian digital news media. In addition to using regular entities from the NER standard, this study introduced new entities specialized for disaster-related information, including DISASTER, SCALE, SUPPLIES, CASUALTIES, and OUTSIDE. The new disaster corpus in the Indonesian language for the NER model was obtained with an imbalanced dataset composition. To overcome this problem, random oversampling was applied. This study also utilized the BiLSTM model to recognize each entity in new textual information, evaluating its performance when the proposed Indonesian disaster corpus was used as a training reference in the deep learning model. Several optimization algorithms applied in BiLSTM were evaluated. The results showed improved BiLSTM performance using Adam optimization and a balanced corpus. Performance indicators achieved were 93.4 %, 82.4 %, and 87.5 % for precision, recall, and F1-score, respectively. The BiLSTM network captured long-range dependencies in sequential data provided by NER. Oversampling ensured that the proposed NER model could precisely recognize all entities and reduce biased results. Thus, the BiLSTM method can better identify entities in the textual corpus of Indonesian disaster-related online news.

Ekphrastic Allusions to Frederick II in the Libro de Alexandre

This article proposes that ekphrases in the Libro de Alexandre allude to Emperor Frederick II’s offences against the papacy, as well as a corpus of pro-papal hierocratic texts. Through comparative readings of Pope Innocent III’s decretal ‘In Genesi legimus’ and selected works of Honorius Augustodunensis, I argue that Stateira’s tomb should be interpreted according to the hierocratic mode of biblical exegesis, which utilizes the four senses of scripture to endow biblical episodes with hierocratic connotations, thereby enabling the tomb to be read as an allegory of arguments for papal primacy in imperial affairs.

Text Classification Based on Natural Language Processing and Machine Learning in Multi-Label Corpus

The rapid development of the Internet has led to a geometric expansion of text information resources online. Among them, corpus, as the basic data source of natural language processing based on statistical language model, its construction and application have become a hot issue in current language processing research. After consulting a large number of relevant literature and materials, it was found that many researchers have provided new ideas for multi label corpus text classification methods. However, this article adds its own understanding and takes this as the direction and basis. In the introduction, the research significance of text classification was introduced, and then academic research and analysis were carried out on the two key sentences of corpus text classification and natural language processing in multi-tag corpus text classification. This article then utilizes an algorithm model to provide a theoretical basis for the study of multi-label corpus text classification methods; At the end of this article, a simulation comparative experiment is conducted, and the experiment is summarized and discussed; In the Enterprise L corpus, the difference in recall rates before and after the use of Entrance 1 was 5.5%, the difference in recall rates before and after the use of Entrance 2 was 7.8%, the difference in recall rates before and after the use of Entrance 3 was 3.3%, and the difference in recall rates before and after the use of Entrance 4 was 4.5%. At the same time, with the continuous research of natural language processing and machine learning, the research on text classification methods of multi tag corpus is also facing new opportunities and challenges.

Masketeer: An Ensemble-Based Pseudonymization Tool with Entity Recognition for German Unstructured Medical Free Text

Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge.

Sexism and Media Communication. An Application to the Italian Case

Acknowledging the importance of focusing on media’s communication for studying linguistic sexism, we propose a new method to analyze a corpus of texts via a machine learning approach built around an original training-set. We seek to establish a framework of the current use of talking about women in newspapers that expands beyond merely the objective forms of discrimination by also measuring the degree to which it implicitly conveys sexist messages through combination of words, expressions, and lexical aspects of language. As an illustrative example, we then apply such an approach to around 15,000 Italian newspapers’ headlines to investigate the impact of newspapers’ political orientations on the linguistic choices made by journalists in writing articles’ headlines.

Fifty Years of Secrecy: The Politics of Oblivion and Perpetuation of the Dictatorship’s Impunity in Contemporary Uruguay

This article explores the long duration of posttransitional authoritarian discourses of “national security” in Uruguay, in the five decades since the coup that led to a State terrorist regime. We posit that the current deployment of Cold war era discourses justifying state terrorism constitute foundational master tools that enable modern-day military and alt-right autocrats to conceal human rights violations and appropriate the liberal human rights discourse for illiberal political ends. Authoritarians of this new type use these resignified democratic discourses to perpetuate their power, gain influence, and legitimize their repressive illiberal practices. The article uses three public debates relating to the military’s illicit actions in the dictatorship and their involvement in contemporary politics via the new military political party, Cabildo Abierto, to show how these authoritarian tools are deployed in political discourse. We analyze a corpus of texts from recent judicial documents with military “confessions,” official withholding of secret archives, and law proposals presented in Congress. Our findings show that dictatorship-era discourses of “national security” are employed by contemporary military and right-wing political actors for the purpose of sustaining their power. We reveal continuities in dictatorship era discourse practices and strategies that contribute to the emergence of new autocratic social actors who enter the political sphere to capture state resources and protect their group interests. Our findings contribute to the exploration of discursive uses of the past in the present resulting in increased political polarization and autocratic tendencies in contemporary Uruguay.

Levkoi, Waschnik, härra Krokus ja teised. Baltisaksa laste mängu- ja kasvamismaailmad

This article focuses on imaginative childhood games and fictional playmates in Baltic German children’s games, as seen in the context of, and in relation to, Baltic German domestic and educational culture. Through reminiscing about these games, a view of the Baltic German domestic environment in all its diversity is opened up. It gives an insight into the mental environment – relationships within the immediate and extended family and domestic circle (which also included servants) and the relationships and interactions beyond, but also shares information about the physical environment – the everyday culture and way of life. And in the midst of it all, of course, the narrators are looking back on their own formative experiences and years. The examples of more or less imaginative games are drawn from the memoirs of nine Baltic German authors, of which one text is available in Estonian translation, while the others are unknown to the Estonian reader as complete texts, and therefore perhaps all the more interesting. The authors have been selected from the corpus of Baltic German memoir texts collected from reading, based on the focus of this article, i.e., texts that also deal with the author’s childhood and where fictional playmates and worlds are also discussed. The selection is certainly not exhaustive, but it provides a preliminary insight into the subject. Baltic German memoir literature, especially concerning the generations whose childhoods passed during a period of stability and peace – whether at the end of the 19th century before Russification, or the pre-World War I era – often exhibits a particular contrast. Childhood and youth were left behind at a time when the world was still in order, when the life as it used to be had not collapsed. Later Baltic Germans very often wrote memoirs with their descendants in mind, in order to create a generational link, to introduce the lost world and way of life, but also to generalise the impact of historical events from the individual level to the national group as a whole. Post-World War II memory work directed at descendants became particularly significant, as everything had once again and irreversibly gone. The examples of imaginative childhood games and fictional playmates in the Baltic German memoirs discussed in this article carry a positive tone, occasionally accompanied by a light-hearted and wise reflection. Children were encouraged to engage in independent play, while not being neglected, especially the younger children, for whom the presence of an ‘interested adult’ in the background was noticeable. Storytelling and literature played a key role in the development of imagination and creativity. The plots of literary works were transferred to games and further developed, and dramatization techniques were employed using readily available resources. In addition to classical and romantic literature, the physical environment – buildings, interiors, the surrounding nature – also exerted important influence. Fictional companions existed both in the case of an only child and in the case of children growing up among siblings. In the latter case, there were often shared fantasy characters and worlds – whether they were just air or on paper. Adults also became part of the imaginary worlds and retained their role as interested observers – as long as the imaginary world did not overflow too much – leaving room for individual originality, which has been considered a distinctive feature of Baltic German society. The authors recall their childhood with fond memories, where home and family formed a stable, secure and vital growing environment, and the necessary resilience and resourcefulness for coping with life’s challenges. As such, these examples also fit into the framework of what Helene Hoerschelmann calls “old-school Baltic German upbringing”. Undoubtedly, the self-aware adaptability acquired during their childhood and youth served them well – the successive pivotal historical events, wars and revolutions became an inevitable part of their lives. Thus, a study of the history of the Baltic Germans’ childhood and upbringing could be of interest not only as part of an unknown or forgotten and lost world, but also as a multifaceted source of reflection and comparison, especially from today’s perspective.

Functioning and translation of English and Russian architectural terms in academic discourse

The study provides the analysis of architectural terminology in English and Russian in academic discourse. The object of the study is the terminological system of architecture. The subject of the study is the specifics of the use, functioning and translation of English and Russian architectural terminology in academic discourse. The classification and systematization of architectural terms, their hierarchical structure and functional types in academic texts are considered. In addition, the interdisciplinary nature of the architectural sublanguage is emphasized, which intersects with terms from the fields such as engineering, architecture, construction, art and design. The paper also identifies existing difficulties and problems that arise when translating English and Russian-language architectural terms, including differences in the linguistic structure of languages, semantic ambiguity, the need for a high degree of specialization of the translator for the correct interpretation of the term, the use of idiomatic and figurative language to describe some architectural terms, etc. The study uses a comprehensive methodology, in particular, methods of comparative and component analysis, as well as methods of corpus linguistics used to study semantic fields, hierarchical relationships and functional types of terms. The research results emphasize the importance of standardization of architectural terminology, understanding of cultural and historical background, as well as technical knowledge to ensure effective communication in the global architectural community. Analyzing the corpus of texts, including dictionaries, architectural reviews and academic articles, the author explains how specific terms function in academic discourse. This study makes a special contribution to the development of linguistic research in the field of architecture, offering an in-depth analysis of semantic fields and their role in structuring architectural vocabulary. The novelty of the research lies in an integrated approach to the study of the functioning and translation of architectural terms, which allows to identify the difficulties associated with the interdisciplinary character of the terminology. The results of the study will be useful not only for teachers and translators, but also for international experts in the field of architecture.

MSAPersonality: a modern standard Arabic dataset for personality recognition

Automatic personality recognition is a task that attempts to automatically infer personality traits from a variety of data sources, including Text. Our words, whether spoken or written, reveal a lot about who we are. As people speak different languages, each with its own set of characteristics and level of complexity, identifying their personalities automatically might be language-dependent. This task requires an annotated text corpus with personality traits. However, the lack of corpora for languages other than English makes the task extremely challenging. We concentrated our efforts in this paper on the Arabic language in particular because it is understudied and lacks a corpus, despite being one of the most widely spoken languages in the world. Our primary goal was constructing our “MSAPersonality” dataset, which consists of 267 texts in modern standard Arabic that have been annotated with the Big Five personality traits. To evaluate the dataset and its potential for classification and regression, we used text preprocessing techniques, feature extraction, and machine learning algorithms. We obtained promising experimental results. Therefore, further research into predicting personality from Arabic text can be conducted.

Text classification for distribution substation inspection based on BERT-TextRCNN model

With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed energy resources, fine-grained perception of intelligent sensing devices, and the inherent stochasticity of source-load dynamics, a massive amount of raw data is being recorded and accumulated in the data center. Valuable information is often dispersed across different paragraphs of the raw data, making it challenging to extract effectively. Distribution substation inspection plays a crucial role in ensuring the safe operation of the power system. Traditional methods for inspection report text classification typically rely on manual judgment and accumulated experience, resulting in low efficiency and a significant misjudgment rate. Therefore, this paper proposes a text classification method for inspection reports based on the pre-trained BERT-TextRCNN model. By utilizing the dense connection between the BERT embedding layer and the neural network, the proposed method improves the accuracy of matching long texts. This article collected 2,831 maintenance data for the first quarter of 2023 from the distribution room, including approximately 58 environmental testing data, 738 environmental box testing data, approximately 672 distribution room testing data, and approximately 1,363 box type substation testing data. A text corpus was constructed for experiments. Experimental results demonstrate that the proposed model automatically classifies a large volume of manually recorded inspection report data based on time, location, and faults, achieving a classification accuracy of 94.7%, precision of 92%, recall of 92%, and F1 score of 90.3%.

Bridging spherical mixture distributions and word semantic knowledge for Neural Topic Modeling

Neural Topic Modeling has attracted significant attention from the Natural Language Processing community due to its black-box inference property and has made some progress. However, existing approaches have three main drawbacks: (1). hard to integrate external semantic knowledge to improve topic quality; (2). the posterior collapse issue of the Variational Auto-Encoder (VAE) harms the topic diversity; (3). cannot capture the semantic scopes of extracted topics. To address these limitations jointly, we model topics with a mixture of von Mises-Fisher distributions to incorporate word semantic knowledge and propose the Spherical Embedded Topic (SET) model based on the Wasserstein Auto-Encoder. Experiments have been conducted on three widely used text corpora, and the results show that SET obtains more coherent and diverse topics, outperforming the state-of-the-art approaches. Moreover, the ability of SET to capture semantic scopes of topics is also validated by our experiments.