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Clustering scientific publications can reveal underlying research structures within bibliographic databases. Graph-based clustering methods, such as spectral, Louvain, and Leiden algorithms, are frequently utilized due to their capacity to…

Literature recommendation systems (LRS) assist readers in the discovery of relevant content from the overwhelming amount of literature available. Despite the widespread adoption of LRS, there is a lack of research on the user-perceived…

Text features that are correlated with class labels, but do not directly cause them, are sometimesuseful for prediction, but they may not be insightful. As an alternative to traditional correlation-basedfeature selection, causal inference…

Social networking services (SNS) contain vast amounts of image-text posts, necessitating effective analysis of their relationships for improved information retrieval. This study addresses the classification of image-text pairs in SNS,…

Since the seminal work of Mikolov et al., word embeddings have become the preferred word representations for many natural language processing tasks. Document similarity measures extracted from word embeddings, such as the soft cosine…

Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher's collaboration patterns, context…

Texts, whether literary or historical, exhibit structural and stylistic patterns shaped by their purpose, authorship, and cultural context. Formulaic texts, characterized by repetition and constrained expression, tend to have lower…

Mental health poses a significant challenge for an individual's well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a…

The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task,…

Dimensions was introduced as an alternative bibliometric database to the well-established Web of Science (WoS) and Scopus, however all three databases have fundamental differences in coverage and content, resultant from their owners'…

Document classification is the detection specific content of interest in text documents. In contrast to the data-driven machine learning classifiers, knowledge-based classifiers can be constructed based on domain specific knowledge, which…

We analyzed Medical Subject Headings (MeSH) from 21.6 million research articles indexed by PubMed to map this vast space of entities and their relations, providing insights into the origins and future of biomedical convergence. Detailed…

Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower…

The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge…

Numerous indexing databases keep track of the number of publications, citations, etc. in order to maintain the progress of science and individual. However, the choice of journals and articles varies among these indexing databases, hence the…

Cloud-based visualization services have made visual analytics accessible to a much wider audience than ever before. Systems such as Tableau have started to amass increasingly large repositories of analytical knowledge in the form of…

Automatic text categorization is a complex and useful task for many natural language processing applications. Recent approaches to text categorization focus more on algorithms than on resources involved in this operation. In contrast to…

We discuss two techniques used to characterize bibliographic records based on their similarity to and relationship with the contents of the NASA Astrophysics Data System (ADS) databases. The first method has been used to classify input text…

Distance-based unsupervised text classification is a method within text classification that leverages the semantic similarity between a label and a text to determine label relevance. This method provides numerous benefits, including fast…

Rapid growth of the biomedical literature has led to many advances in the biomedical text mining field. Among the vast amount of information, biomedical article abstracts are the easily accessible sources. However, the number of the…