Improving the Quality of the Relevance of the Search for Scientific Publications Based on a Combination of Ranking Methods
Year: 2023
Type: conference-paper
Abstract: The study aims to develop a combined method for finding relevant documents using automated Nature Language Processing (NLP), which improves the quality of results based on informative text parameters. To adapt the search method to the text type, it is necessary to combine several algorithms for processing sets of texts. Thus, results of a search in sets of documents should provide the analysis of separate components of the text, especially interesting for the analyst. To achieve this goal, the authors: conducted a brief review of the status of the issue in the field of NLP and full-text search; classified the methods and means of word processing available to Data Science specialists; chose the most promising characteristics of the texts in terms of the purpose of the work. The effectiveness of joint ranking of specialized articles devoted to the solution of specific scientific issues and articles- reviews of the subject area of large volume in terms of their relevance to the analyst's request was studied. The possibility of using the reference text chosen by the analyst as a basic standard for querying and searching and ranking similar scientific and technical documents is considered. Experiments with text sets were performed, which allowed confirming the informativeness of the parameters of text objects selected by the authors and the proposed composition of algorithms for their processing based on a combination of TF-IDF method and relevance ranking method using the distance between term occurrences.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7166294665