Text Collections Research Articles

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.

HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.

With the exponential growth of the life sciences literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. The identification of entities in texts, such as diseases or genes, and their normalization, i.e. grounding them in knowledge base, are crucial steps in any BTM pipeline to enable information aggregation from multiple documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied "in the wild," i.e. on application-dependent text collections from moderately to extremely different from those used for training, varying, e.g. in focus, genre or text type. This raises the question whether the reported performance, usually obtained by training and evaluating on different partitions of the same corpus, can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for entity recognition and normalization, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five, based on predefined criteria like feature richness and availability, for an in-depth analysis on three publicly available corpora covering four entity types. Our results present a mixed picture and show that cross-corpus performance is significantly lower than the in-corpus performance. HunFlair2, the redesigned and extended successor of the HunFlair tool, showed the best performance on average, being closely followed by PubTator Central. Our results indicate that users of BTM tools should expect a lower performance than the original published one when applying tools in "the wild" and show that further research is necessary for more robust BTM tools. All our models are integrated into the Natural Language Processing (NLP) framework flair: https://github.com/flairNLP/flair. Code to reproduce our results is available at: https://github.com/hu-ner/hunflair2-experiments.

ANALISIS VERBA ‘PUKUL’ DALAM KORPUS BAHASA INDONESIA: STUDI LINGUISTIK KORPUS

This research compares the derivatives of the word "ajar" in data in the Corpus Query Processor: LCC Indonesia 2023 and the latest edition of the Indonesian Dictionary (KBBI). The derivatives of the word "teach" that are identified are the differences and similarities in sentence patterns, sentiments and types of sentences. Corpus Query Processor: LCC Indonesia is a collection of digital texts that represent a particular language. This study is included in corpus linguistics studies, namely empirical studies of language using features such as concordance, collocations, N-grams, and word lists. The method used is a descriptive qualitative approach with in-depth analysis of lingual corpus data. The research results found 13 derivatives of the word "teach" in the verb class in the corpus, with 5 of them found in KBBI Edition VI. This difference in derivatives of the word "teach" indicates the possibility that some commonly used derivatives of the word have not been included in the KBBI. Another finding is that the sentence pattern of each derivative of the word "teach" has been clearly identified, indicating the subject, predicate, object, as well as any additional information that may be present. Actor and patient sentiments vary depending on the context of the sentence. However, generally, there is a positive sentiment towards the derivative of the word "teach", which indicates the act of learning or teaching that is considered good or useful.

Transformer-Based Topic Modeling for Urdu Translations of the Holy Quran

Topic modeling enables the discovery of concealed themes and patterns in extensive text collections. It facilitates a thorough examination of the messages present in religious texts. Topic modeling for Quranic verses is a trending study area, with various translations already explored including Bahasa, English, and Arabic. Yet, there is a need for further research, particularly in Urdu translations of the Quran. In this study, we propose applying the BERTopic framework to Urdu translations of The Holy Quran. By leveraging the BERTopic approach, which incorporates a fine-tuned BERT model, we aim to capture the contextual nuances and linguistic complexities unique to the Quran. In this study, we utilized existing Urdu translations of the Quran from eight different translators sourced from Tanzil, a renowned resource for Quranic text and translations. We assessed the performance of our proposed BERTopic model compared to traditional techniques like LDA and NMF, using coherence and diversity metrics. The results indicate that our BERT-based approach outperforms these conventional methods, achieving an average coherence improvement of 0.03 and a diversity score of 0.83. These findings highlight the effectiveness of BERTopic in extracting meaningful topics from Urdu translations of The Holy Quran and contribute to the computational analysis of religious texts, supporting scholarly endeavours in comparative studies of Quranic translations in Urdu.

Psycho-semantic analysis of job advertisements as a communicative tool of educational marketing

Introduction. The relevance of the study of job advertisements for teachers and psychologists is associated with the need to find tools for regulating communication within the modern educational environment with dynamic, constantly changing working conditions. The publication of employer requirements sets the direction for changing the criteria for the success of the specialist, which changes the entire educational environment. The purpose of the study: to identify the categorical semantic structure of job advertisements for teachers (using the example of the "Infrastructure of Scientific Research Data" platform). Materials and methods. The object of the study was a corpus of teacher job vacancies from the "Infrastructure of Scientific Research Data" platform, published in 2023 in 83 regions of Russia (5561 vacancies). The job postings included descriptions of candidate requirements, information about the offered salary, and the region in which the vacancy was available. As a comparison group, a corpus of psychologist job vacancies (2076 vacancies) was considered. The following methods were used in the study: 1) Latent Dirichlet Allocation (LDA) method for identifying and analyzing the structure of requirements presented in teacher and psychologist job vacancies; 2) Term Frequency-Inverse Document Frequency (TF-IDF) method for categorical semantic analysis of the structure of teacher and psychologist job vacancies and description of the semantic framework (frame) of the job description; 3) Descriptive statistical methods for analyzing the needs of professionals in helping professions in different regions of Russia and the conditions offered to them. The results of the study. The regions of the Russian Federation with the greatest need for teachers and psychologists have been identified. Based on the statistical significance in the collection of text documents, the most demanded skills of teachers by employers are identified: methodological development and design of an individual educational route (0.167), work with children with disabilities (0.144), organization of the educational process (0.089). For vacancies of psychologists, the most significant were: work on investigative actions (0.319), methodical work in an educational organization (0.187), psychological and pedagogical support for successful development and training (0.129). The main meanings governing professional pedagogical activity are more focused on meta-skills based on the values of inclusion and continuous improvement of education, as well as on flexible skills (Soft skills) that increase competitiveness in various fields of pedagogical activity. In the vacancies of psychologists, professional skills (Hard skills) are relevant, applicable in specific areas, which somewhat narrows the scope of professional implementation. Conclusion. The results of the empirical study allow for considering job vacancy advertisements as one of the communicative tools of educational marketing. Analyzing the significant professional skills from the employer's perspective will enable the development of recommendations for the development of professional communication for teachers, which can be considered in the system of continuous professional education, as well as in regulating interaction within the educational environment. Automated analysis of job vacancies can become a successful interactive diagnostic tool for educational marketing, necessary for the development of the educational space and the improvement of the competitiveness of educational institutions.

Bridging Linguistic Gaps: Developing a Greek Text Simplification Dataset

Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique challenges that hinder comprehension for native speakers, learners, tourists, and international students. This paper introduces a comprehensive dataset for Greek text simplification, containing over 7500 sentences across diverse topics such as history, science, and culture, tailored to address these challenges. We outline the methodology for compiling this dataset, including a collection of texts from Greek Wikipedia, their annotation with simplified versions, and the establishment of robust evaluation metrics. Additionally, the paper details the implementation of quality control measures and the application of machine learning techniques to analyze text complexity. Our experimental results demonstrate the dataset’s initial effectiveness and potential in reducing linguistic barriers and enhancing communication, with initial machine learning models showing promising directions for future improvements in classifying text complexity. The development of this dataset marks a significant step toward improving accessibility and comprehension for a broad audience of Greek speakers and learners, fostering a more inclusive society.

A Knowledge Graph Embedding Model for Answering Factoid Entity Questions

Factoid entity questions (FEQ), which seek answers in the form of a single entity from knowledge sources such as DBpedia and Wikidata, constitute a substantial portion of user queries in search engines. This paper introduces the Knowledge Graph Embedding model for Factoid Entity Question answering (KGE-FEQ). Leveraging a textual knowledge graph derived from extensive text collections, KGE-FEQ encodes textual relationships between entities. The model employs a two-step process: (1) Triple Retrieval, where relevant triples are retrieved from the textual knowledge graph based on semantic similarities to the question, and (2) Answer Selection, where a knowledge graph embedding approach is utilized for answering the question. This involves positioning the embedding for the answer entity close to the embedding of the question entity, incorporating a vector representing the question and textual relations between entities. Extensive experiments evaluate the performance of the proposed approach, comparing KGE-FEQ to state-of-the-art baselines in factoid entity question answering and the most advanced open-domain question answering techniques applied to FEQs. The results show that KGE-FEQ outperforms existing methods across different datasets. Ablation studies highlights the effectiveness of KGE-FEQ when both the question and textual relations between entities are considered for answering questions.

THE IMAGE OF A POSITIVE HERO IN THE UKRAINIAN MAGICAL TALE

The article examines the features of the image of a positive hero of a magical fairy tale, its content, context, and educational potential. The purpose of this article is to study of the characteristic features of the image of the positive hero of the Ukrainian fairy tale and the identification of his educational potential. The study of the image of the positive hero in Ukrainian magical fairy tales employs a multidisciplinary approach, incorporating folklore studies, literary criticism, and cultural anthropology. This method allows for an in-depth examination of the narrative structures, thematic elements, and cultural significance of the tales. The primary sources for this study include a comprehensive collection of Ukrainian magical tales, scholarly articles, and historical texts that provide insights into the evolution of these stories and their roles within Ukrainian culture. The types of heroes of magical tales are highlighted, their national and universal virtues are analyzed. Mythologemes and archaisms of the fairy tale "Kotygoroshko" were studied. It was concluded that the images of positive heroes of fairy tales in their own actions, activities, exploits, communication with other characters appear as integral human characters, exponents of the best virtues of the Ukrainian people. Heroes are consistent in their actions, value honor, justice, loyalty to family, people, and Motherland above all else. They wage an implacable struggle against evil in all its forms and always win. These are strong, courageous, honest, reliable knights, devoted to the end of the fight for the truth.

Resolving the Paradoxism with the Logic of "Not" in Javanese Puppetry: A Re-reading of Wrekodoro and Dewaruci’s Mythology

There are a large number of text collections in Indonesia, related to wayang theme, cf Zoetmulder (1971). And the text collections do not consist only of wayang purwa but also variations of this theme, including wayang wahyu etc. In the mean time, a Japanese scholar Shoshichi Nagatomo proposed the concept of "Asian Logic" which differs from Western logic. In Western thought, things are often seen as black or white, good or bad. However, Asian logic embraces certain degree of ambiguity. Here, "good" characters can exhibit flaws, and "bad" characters can possess redeeming qualities; especially among Javanese people preference is given more to harmony with others and with Nature (called “serasi” or “cocok”). This concept resonates with the Javanese philosophy of "ngono yo ngono neng ojo ngono" (note: the phrase can be translated: “you can do what you like, but you should not to” – as in paradoxism). This philosophy can be considered as embodiment of the logic of "not," acknowledging the contradictory multifaceted nature of reality. This review article is a follow up to our previous articles [1, 12, 13].

Exploring Qualitative Geographies in Large Volumes of Digital Text: Placing Tourists, Travelers, and Inhabitants in the English Lake District

Thus far, approaches to analyzing geographies in large collections of digital texts have used coordinate-based locations derived from toponyms (place names). Although this approach provides a useful starting point, it ignores both nontoponym references to place and textual structure while stressing Euclidean space. This article addresses these issues. Focusing on a corpus of writing about the English Lake District before 1900, it explores the different geographies associated with tourists, travelers, and inhabitants. Alongside toponyms, we identify the concept of a geo-noun to associate place with geographical features. Sense of place is then identified using the adjectives, nouns, and verbs that writers associate with locations and locales. This methodology allows us to identify and define places described in large volumes of digital text both holistically and textually.

Exploring topics of the female librarians

Background of the study: Female librarians often face limitations in their professional development and encounter various challenges. Previous studies have shown that while many articles focus on women librarians as a subject, few delve into the topics discussed. Purpose: This research aims to find out which topics are developing in the world of libraries, with a specific focus on female librarians. Method: This study uses topic modelling to explore abstracts from documents discussing female librarians, using BERTopic, scattertext, and VOSviewer to identify emerging topics from data obtained from Scopus. Findings: A total of 6 topics were determined, where Topic 0 and Topic 3 had the highest similarity. At the same time, keyword analysis did not reveal any particularly prominent keywords in the 2020s. Conclusion: The discussion on female librarians covers topics such as professional advancement, work-life balance, knowledge gaps in technology, stereotypes, and the correlation between these topics. This study provides an overview of text analysis that librarians can use to identify topics in a collection of texts, such as abstracts, and examine how different topics relate to each other, as a single document can reflect multiple topics.

SrpELTeC: A Serbian Literary Corpus for Distant Reading

The article presents SrpELTeC, a corpus developed within the COST action Distant Reading for European Literary History (CA16204). All novels in SrpELTeC were selected, prepared, and annotated using the common principles established for all language collections in the European Literary Text Collection (ELTeC). The challenges and solutions in preparing SrpELTeC from scratch are outlined. All novels were manually encoded in TEI with rich metadata and structural annotation. The automatic annotation included POS-tagging, lemmatization, and named entities, relying on Natural Language Processing resources developed and maintained by the JeRTeh Language Resources and Technologies Society. The integration of SrpELTeC with Wikidata was supported with a set of SPARQL queries for the retrieval of metadata with different visualization options. Recent activities within the COST Action NexusLinguarum—European Network for Web-centred Linguistic Data Science (CA18209) are related to the linked data version of SrpELTeC using the NLP Interchange Format. All versions of SrpELTeC are freely available under the CC-BY license.

Alternative interpretations of manifold texts: the case of Mircea Eliade’s political writings

ABSTRACT The interpretation of extensive collections of texts is central to the history of religions. Bruce Lincoln’s Secrets, Lies, and Consequences paints a decisive picture of Mircea Eliade based on the latter’s articles from 1932 to 1939. Before his death Mac Linscott Ricketts donated the contents of his office to me and to Liviu Bordaş and I am in possession of his many translations of Eliade’s Romanian writings otherwise unavailable in English. With reference to all of these translations a very different picture of Eliade’s position can be suggested than that proposed by Lincoln. The relevance of Moshe Idel’s 2014 analysis of Eliade is also considered. Conflicting interpretations can best be resolved by open access to primary sources. Ricketts’ translations – and more – need to be made available for non-Romanian readers to make their own assessments.