Text Collections Research Articles

Performance Comparison of Text Weighting Schemas on NMF-Based Topic Analysis

Nowadays, it is feasible to analyze text data that is being generated at an exponential rate by transforming it into a sparse matrix of big size using a certain weighting method. A comprehensive text weighting approach consists of three fundamental components: Term Frequency, Document Frequency, and Vector Normalization. The multiplication of these three components yields numerical values that indicate the significance of a word for a text. Nevertheless, the unprocessed state of these values is unsuitable for the semantic analysis of textual material. There are multiple techniques available for this objective, and Topic Analysis, which seeks to identify subjects discussed in extensive text collections, is one of these techniques. The Non-Negative Matrix Factorization (NMF) approach is commonly employed in topic analysis. It involves transforming an input matrix into the product of two or more matrices, using both random and deterministic beginning values. This study involved conducting tests on a dataset of 20,000 articles sourced from Wikipedia, the online encyclopedia, with the aim of investigating the impact of text weighting methods and initial value approaches commonly employed in the literature on the NMF method. The number of clusters to be used in the studies was determined using an analytical procedure, which employed an upper limit. The results indicate that the “lnc” and “nnc” weighting schemes yielded the highest performance in NMF. These findings demonstrate that employing the “lnc” or “nnc” weighting scheme will lead to more favorable outcomes in the domain of topic analysis.

Comments on School Education in Mathematics and Computer Science

This work aims to improve problem-solving skills even for students and adults, who are not so interested in mathematics. This requires simple algorithms. The efficiency of these is not the focus of this work, as we assume that computers will continue to take over this computational work. We concentrate on important, flexible operations that are as universal as possible. In doing so, we rely on the object of the structured table. A structured table can represent a number of any type, but also texts and structured collections of text and numbers. This means that the focus is no longer only on individual data operations such as addition, multiplication, sine, root, etc., but also on mass data operations such as selecting (choosing), sorting, aggregating, restructuring, joining (merging tables), etc. The work clearly shows that these operations can also be used to develop new, simpler solutions for the problems now being dealt with at school. Simpler, more universal algorithms that can be programmed by anyone should be taught at school. The work on the data model o++o was strongly motivated by the Relational data model with its language SQL and the language CONVERT, which tried to extend the flat table model to hierarchical data structures.

"My Very Subjective Human Interpretation": Domain Expert Perspectives on Navigating the Text Analysis Loop for Topic Models

Practitioners dealing with large text collections frequently use topic models such as Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) in their projects to explore trends. Despite twenty years of accrued advancement in natural language processing tools, these models are found to be slow and challenging to apply to text exploration projects. In our work, we engaged with practitioners (n=15) who use topic modeling to explore trends in large text collections to understand their project workflows and investigate which factors often slow down the processes and how they deal with such errors and interruptions in automated topic modeling. Our findings show that practitioners are required to diagnose and resolve context-specific problems with preparing data and models and need control for these steps, especially for data cleaning and parameter selection. Our major findings resonate with existing work across CSCW, computational social science, machine learning, data science, and digital humanities. They also leave us questioning whether automation is actually a useful goal for tools designed for topic models and text exploration.

Texts of Different Emotional Classes and Their Topic Modeling

The article is devoted to studying verbalization specifics of various emotional states in the texts in the Russian language with the purpose to confirm or refute the hypothesis that texts of different emotional classes reflect the denotative situation not identically, which is reflected in thematic specifics and lexical content. The research material consisted of eight corpus texts in the Russian language, which were extracted from the public pages of the social network VKontakte. The texts were selected according to emotional hashtags that corresponded to eight basic emotions, according to H. Lцvheim’s model: anger, surprise, shame, enjoyment, disgust, distress, excitement, fear. The correspondence of emotion and hashtag was established in a preliminary psycholinguistic experiment. While analyzing the text collection, we used the method of computer thematic modeling to identify statistically non-random groups of words (topics). We applied the BERTopic neural network model to the collected data. As a result of the analysis, it was found that texts of 8 emotional classes contain an uneven number of topics, despite the fact that their number does not correlate directly with the amount of data: with a relatively small amount of data, there may be many topics, but in a voluminous corpus – few. The sets of words (tokens) that make up each non-random group (topic) differ in each subcorpora, reflecting the specifics of the denotative situation, which is formed under the influence of the emotional state of the speaker. The idea of diverse thematic “granularity” of texts of different emotional classes is theoretically justified.

‘Risala Tahaffuz Min al-Nazla’, Al-Rāzi’s Legacy in Cold Remedy and Prevention

Nazla (Catarrh) is a condition in Unani medicine that involves the flow of catarrhal fluids, or mucus, from the brain downwards into the throat and chest, leading to symptoms similar to what we consider a cold today. This condition is characterized by congestion, irritation in the respiratory pathways, and in some cases, can progress to more serious respiratory complications if left untreated. While preparing a detailed catalogue of Unani medical manuscripts housed at the Telangana Government Oriental Manuscripts Library and Research Institute (TGOMLRI) in Hyderabad, a significant discovery was made: a concise yet vital manuscript titled Risala Tahaffuz min al-Nazla (A Treatise on the Prevention of Cold and Catarrh), cataloged under Accession No. 8880. This manuscript, written by the eminent physician Muhammad ibn Zakariyyā al-Rāzi (865–925 AD), offers invaluable insight into the traditional understanding and prevention of the common cold. Al-Rāzi, also known as Rhazes in the Latin world, was one of the most influential figures in the history of medicine, contributing profoundly to clinical practices and medical theories that remained relevant for centuries. This treatise, written in Arabic, reflects al-Rāzi's keen focus on preventive medicine, specifically on measures to avoid the onset of cold and catarrhal conditions. It is a paper manuscript, notable for its brevity, consisting of only 8 pages. Each page contains 14 lines, with an average of 16 letters per line, displaying a compact and highly structured layout that suggests its use as a practical guide. Its focus on prevention rather than solely treatment is particularly notable, as it aligns with al-Rāzi’s broader medical philosophy emphasizing lifestyle and precautionary health measures. Moreover, Risala Tahaffuz min al-Nazla outlines remedies based on herbal medicine, prescribing specific herbs and natural compounds known to alleviate symptoms of cold and prevent its occurrence. These herbal prescriptions include ingredients that are easily available in Unani medicine and have historically been used for their anti-inflammatory, expectorant, and warming properties, which can relieve respiratory congestion and discomfort. Interestingly, another copy of this treatise exists in Iran, underscoring the historical and scholarly connections between Indian and Persian centers of Unani medicine. This manuscript’s discovery in Hyderabad adds to the rich collection of Unani medical texts in India, showcasing the traditional medical knowledge that continues to be studied for its relevance to modern alternative medicine practices. Through such manuscripts, Unani medicine remains an enduring part of the cultural and scientific heritage in regions with strong historical ties to Persian medical traditions. The concise nature of Risala Tahaffuz min al-Nazla and its practical content makes it a valuable text for both historical research and contemporary applications, providing insights into how ancient Unani physicians approached the common cold with holistic, plant-based remedies.

Черно море като политическа и културна метафора. (Наблюдения върху „Червената Ривиера“ на Кристен Годси и „Златните прахоляци“ на Силвия Шедлецка)

The article analyzes the image of Black Sea littoral as a cultural and political metaphor for the processes in Bulgaria’s communist and post-communist period. Two outside critical perspectives on the recent Bulgarian history are compared – Kristen Ghodsee’s ethnographical study based on small histories of Bulgarians working in the tourism industry, whose lives were affected by the Transition period, and Sylwia Siedlecka’s collection of non-fiction texts on the physical and mental remnants of communist past, which continue to shape the present. The article outlines the image of the pre- and post-1989 period presented in the books, arguing that in both cases the sea metaphor (Red Riviera for the communist seaside project and Golden Dusts for the remains of the communist past in the post-communist present) challenge the common foreign stereotypes about Bulgaria (i.e., the Black Sea resorts, the touristic attractions).

Character Narratives in Indonesian Digital Fiction: A Semi-Digital Stylistics Approach

Digital fiction is a collection of art texts in the form of short stories and novels that combine technology, virtual space, media elements, and provide access to readers or netizens to carry out certain activities in it. Narratology of Characterization is the initial foundation in research in the field of literary studies. Narratology is a field of study that explains narratives and narrative structures in a discourse context. Narratology of characterization talks about the position or scheme of characters in the story. The position of the character talks about the role and impact of the role of the character. The character gives birth to characterization. Characterization gives birth to implications. Implications provides birth to messages and perspectives that will be captured by the reader. Narratology research in digital fiction is research that aims to explain the position, role, and function of the character and characterization itself. This study found that in digital fiction, characters are dominated by women. Female characters in digital fiction hold various roles and impacts on the roles they play. Not only that, this study also found that all works included in the digital fiction category are works that are ready to be owned by many people. Digital fiction does not only belong to the author but also to the reader who acts as a ghost writer who has the right to make decisions or provide suggestions and criticisms of the contents of the story. The author is still recognized as the creator of the story and netizens still take their position as figures who assess the work subjectively and objectively, subjective because there are thoughts, ideas, personal points of view in assessing a fiction, objective in providing assessments, input, suggestions, and criticisms for the maturity or goodness of the story.

A Daring Book

Abstract In 1612, Toussaint du Bray, a renowned Parisian bookseller, publishes a daring anthology: Le Recueil des plus excellents ballets de ce temps, a collection of texts borrowed from ballets of his time. In this article, the author tries to highlight the fact that Toussaint du Bray’s approach constitutes an extremely strong gesture that decisively contributes to the emergence of a true editorial genre. While attempting to address this question, the author will also seek to assess the political significance of this selection of ballets.

SLAVENSKA MITOLOGIJA KAO MEDIJ POPULARIZACIJE BAŠTINSKE KULTURE NA PRIMJERU TEKSTOVA JASNE HORVAT

The paper takes a cultural-philological perspective to examine, i.e., map the Slavic mythological corpus of children’s literature by Jasna Horvat, a writer, multimedia artist and scientist from Osijek: the collection of plays Izgubljena vila (2002), illustrated poetic-prose series of stories Alemperkina kazivanja (2005), novel for youth Krijesnici (2009), illustrated collection of dramatic texts Ars igrum (2021) and picture book Perunika (2023). Furthermore, a perspective is established with regard to the author’s conceptual novels layered on a mythological weave – Vilikon (2012) and Vilijun (2016). The goal of the study is to confirm the thesis that mythology in the above texts is a heritage culture (Old Croatian and Old Slavic) popularization medium aimed at different groups of recipients, primarily the population of young readers, through the process of genre recoding (science – literature).

Genealogy of Being in Postcolonial Kazakh Culture: Re-Reading the Words of Edification

ABSTRACT Contemporary post/decolonial discourses in Kazakhstan have completely excluded the following issue from the field of narrative: the question of critically reevaluating the pre-colonial cultural structure. These discourses consider current issues mostly as a result of colonialism and overemphasize external factors. It is important to read and decipher the symbols of the cultural structure to understand the origins of authoritarianism and other socio-political problems. This article aims to investigate the origins of notions of the human being and its existence in the postcolonial Kazakh culture through comparing it to its pre-colonial cultural context. I will be working with Abai’s Words of Edification, which is a collection of texts written in prose in the form of a free philosophical meditation, and it is an apt source for the investigation of the origin of the notion of human existence. The politicization of Abai’s figure starting from the Soviet period and continuing in the postcolonial era obscures the initial questions in the text, and several lines from the Words of Edification engendered the utmost controversy and overshadowed the rest of the text. The aim of this article is to demythologize the poet’s image and focus on symbols hitherto unread.

Still No Evidence for an Effect of the Proportion of Non-Native Speakers on Natural Language Complexity.

In a recent study, I demonstrated that large numbers of L2 (second language) speakers do not appear to influence the morphological or information-theoretic complexity of natural languages. This paper has three primary aims: First, I address recent criticisms of my analyses, showing that the points raised by my critics were already explicitly considered and analysed in my original work. Furthermore, I show that the proposed alternative analyses fail to withstand detailed examination. Second, I introduce new data on the information-theoretic complexity of natural languages, with the estimates derived from various language models-ranging from simple statistical models to advanced neural networks-based on a database of 40 multilingual text collections that represent a wide range of text types. Third, I re-analyse the information-theoretic and morphological complexity data using novel methods that better account for model uncertainty in parameter estimation, as well as the genealogical relatedness and geographic proximity of languages. In line with my earlier findings, the results show no evidence that large numbers of L2 speakers have an effect on natural language complexity.

Sorry , shit and wow : a case study of the handling of interjections in three Nordic monolingual dictionaries

Abstract This paper discusses a qualitative and, to some extent, comparative metalexicographical case study on interjections, with an English origin, in three Nordic monolingual dictionaries. In short, the study answers the following research questions: (1) How are three well-established interjections handled in The Contemporary Dictionary of the Swedish Academy (SO) compared to corresponding entries in a Danish and a Norwegian dictionary and how can the SO descriptions be developed?; (2) How can three less-established interjections be analyzed and described in an updated version of the SO? The point of departure for answering the research questions is information types that are common in dictionary entries. Furthermore, the use of interjections in corpora and text collections for Swedish are crucial for the investigation. The study shows that interjections as a category imply several challenges for lexicographers. Finally, some suggestions are presented concerning the way in which the description of interjections in the SO may be developed.

Troubling the Waters:

This article provides an overview of “Water as Resource, Relative and Place”: an online collection of eco texts including picturebooks, chapter books, YA novels, and media texts that illustrate a wide range of relationships with water. The article includes a brief history on how the online resource was started and reflects on how we all wear multiple hats in how we relate to water. In better understanding our multifaceted relationship to water, we can better position ourselves in our teaching about, caring for, and flourishing with water.

Computing MEMs and Relatives on Repetitive Text Collections

We consider the problem of computing the Maximal Exact Matches (MEMs) of a given pattern \(P[1\mathinner{.. }m]\) on a large repetitive text collection \(T[1\mathinner{.. }n]\) over an alphabet of size \(\sigma\) , which is represented as a (hopefully much smaller) run-length context-free grammar of size \(g_{rl}\) . We show that the problem can be solved in time \(O(m^{2}\log^{\epsilon}n)\) , for any constant \(\epsilon\gt0\) , on a data structure of size \(O(g_{rl})\) . Further, on a locally consistent grammar of size \(O(\delta\log\frac{n\log\sigma}{\delta\log n})\) , the time decreases to \(O(m\log m(\log m+\log^{\epsilon}n))\) . The value \(\delta\) is a function of the substring complexity of \(T\) and \(\Omega(\delta\log\frac{n\log\sigma}{\delta\log n})\) is a tight lower bound on the compressibility of repetitive texts \(T\) , so our structure has optimal size in terms of \(n\) , \(\sigma\) , and \(\delta\) . We extend our results to several related problems, such as finding \(k\) -MEMs, MUMs, rare MEMs, and applications. Categories and Subject Descriptors: E.1 [Data structures] ; E.2 [Data storage representations] ; E.4 [Coding and information theory]: Data compaction and compression; F.2.2 [Analysis of algorithms and problem complexity] : Nonnumerical algorithms and problems— Pattern matching, Computations on discrete structures, Sorting and searching

Contextual embedded text summarizer system: A hybrid approach

AbstractSelecting crucial sentences from a document is a pivotal task in automatic text summarization systems. Abstractive summarization involves rephrasing key content through advanced natural language techniques, generating a concise, new text conveying critical information. Conversely, extractive summarization reproduces important material from the original text. In the proposed method, a hybrid ensemble approach combines BERTsum for extractive summarization and Longformer2Roberta for abstractive summarization for generating a contextual semantic rich summary for a huge collection of text. These proposed system‐generated summaries were evaluated against reference summaries using the ROUGE package at three rouge levels (Rouge‐1, Rouge‐2, and Rouge‐L). The Proposed contextual embedded hybrid text summarization model has shown significant performance improvement in multiple levels of Rouge score and word mover distance (WMD) of generated summary with a reference summary. The proposed hybrid model demonstrates superior performance over existing state‐of‐the‐art summarizing models on three distinct datasets CNN dataset, WikiSum, and Gigaword dataset. The proposed hybrid model as a text summarizer involves leveraging its capabilities to process longer sequences of text with domain‐specific contextual summaries. This transformers‐based text summarization model has great potential in developing expert systems in various research domains such as health decision support systems, the education sector, customer support chatbots, financial analysis investment recommendations, and financial assistance.

Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.

Introductory note: the 25 April as seen from abroad

The 50th anniversary of the 25 April has been commemorated in 2024. This special issue of the journal Relações Internacionais evokes this event, bringing together a collection of texts presenting different perspectives and approaches on how the events in Portugal were welcomed in various countries.

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.

Near-Duplicate Text Alignment with One Permutation Hashing

This paper studies the near-duplicate text alignment problem under the constraint of Jaccard similarity. Specifically, given a collection of long texts and a short query text, this problem finds all the subsequences in each text whose Jaccard similarities to the query are no smaller than a given threshold. Near-duplicate text alignment is computationally intensive. This is because there are O(n 2 ) subsequences in a text with n tokens. To remedy this issue, a few recent studies propose to first generate the min-hash sketch of every subsequence in each text and then find all the subsequences whose min-hash sketches are similar to that of the query. They introduce the concept of "compact windows" and show that the O(n 2 k) min-hashes in a text with n tokens can be losslessly compressed in compact windows using O(nk) space, where k is the sketch size. However, the space cost O(nk) is still too high for long texts, especially when the sketch size k is large. To address this issue, we propose to use One Permutation Hashing (OPH) to generate the min-hash sketch and introduce the concept of "OPH compact windows". Although the size of each sketch remains the same, which is O(k), we prove that all the O(n 2 k) min-hashes generated by OPH in a text with n tokens can be losslessly compressed in OPH compact windows using only O(n+k) space. Note the generation of OPH compact windows does not necessitate the enumeration of the O(n 2 k) min-hashes. Moreover, we develop an algorithm to find all the sketches in a text similar to that of the query directly from OPH compact windows, along with three optimizations.We conduct extensive experiments on three real-world datasets. Empirical results show our proposed algorithms significantly outperformed existing methods in terms of index cost and query latency and scaled well.

Ultimate Fantasy. Subwersyjne strategie queerowania Sztucznej Inteligencji w projektach transliterackich

This article describes artistic examples of queering Artificial Intelligence (AI), which can be understood as a practice that challenges the (hetero)normative nature of neural networks by implementing a queer perspective. The evaluation of ways in which AI reflects societal norms is conducted by addressing biases that disproportionately affect marginalized communities. On this basis, the author highlights the need for inclusive practices that make it possible for queer experiences to be represented by technology while also discussing why AI cannot be neutral. The in-depth study of Ultimate Fantasy, a collection of literary texts generated by a Queer AI, reveals how artists use language models in novel, subversive, and possibly liberating ways. The author aims to frame this practice as an example of a queer utopian aesthetic project in accordance with José Muñoz’s understanding of queerness. The various motifs evident in the short story collection – such as queer modes of love or trauma – are analyzed as reflections of themes present in the corpus of queer texts used to train the language model.