Open AccessRanking of documents of topical corpus according to their mutual relevance in the problem of estimating of affinity of a text to the sense standard
Author(s) -
D. V. Mikhaylov,
G. M. Emelyanov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2052/1/012027
Subject(s) - phrase , relevance (law) , natural language processing , sentence , ranking (information retrieval) , computer science , artificial intelligence , value (mathematics) , text corpus , information retrieval , machine learning , political science , law
The offered paper is devoted to the problem of oneness and integrity of image for the semantic pattern (i.e., sense standard) revealed phrase by phrase for some text within a topical collection. One phrase corresponds here to an extended natural-language sentence. The basis of estimating affinity to the standard is the classifying of words of each phrase in a text according to the TF-IDF value relative to some text corpus. Texts to the corpus are pre-selected by an expert. The essence of the problem: for each phrase, its maximal affinity to the sense standard is achieved concerning the individual corpus document, and, consequently, it is necessary to estimate the mutual relevance of such documents concerning different phrases of the analyzed text. Based on distances between vectors of TF-IDF for words of a separate phrase obtained relative to different corpus documents, the significance estimation for each such document is entered into consideration to choose a pair of mutual relevant.