Open AccessOn the Synonym Search Model
Author(s) -
Olga Muratovna Ataeva,
V. A. Serebryakov,
Natalia Pavlovna Tuchkova
Publication year - 2022
Publication title -
èlektronnye biblioteki
Language(s) - English
Resource type - Journals
ISSN - 1562-5419
DOI - 10.26907/1562-5419-2021-24-6-1006-1022
Subject(s) - word2vec , computer science , information retrieval , search engine indexing , synonym (taxonomy) , basis (linear algebra) , relation (database) , digital library , semantic search , selection (genetic algorithm) , web search query , search engine , data mining , artificial intelligence , biology , genus , art , botany , geometry , mathematics , poetry , literature , embedding
The problem of finding the most relevant documents as a result of an extended and refined query is considered. For this, a search model and a text preprocessing mechanism are proposed, as well as the joint use of a search engine and a neural network model built on the basis of an index using word2vec algorithms to generate an extended query with synonyms and refine search results based on a selection of similar documents in a digital semantic library. The paper investigates the construction of a vector representation of documents based on paragraphs in relation to the data array of the digital semantic library LibMeta. Each piece of text is labeled. Both the whole document and its separate parts can be marked. The problem of enriching user queries with synonyms was solved, then when building a search model together with word2vec algorithms, an approach of "indexing first, then training" was used to cover more information and give more accurate search results. The model was trained on the basis of the library's mathematical content. Examples of training, extended query and search quality assessment using training and synonyms are given.