Enhancing information retrieval with semantic query expansion: a Word2Vec-based approach

Wei Yang; Yong Pan

doi:10.1117/12.3012203

21 December 2023 Enhancing information retrieval with semantic query expansion: a Word2Vec-based approach

Wei Yang, Yong Pan

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701M (2023) https://doi.org/10.1117/12.3012203
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

A significant area of research in the field of information retrieval is query expansion. To address the issue of inaccurate user query descriptions and mismatches with query terms during the information retrieval process, a semantic query expansion method based on Word2Vec is proposed. The method involves training low-dimensional word vectors using the Word2Vec distributed neural language probability model, selecting a candidate set of expansion words, and employing a query vector generation approach that focuses on the expansion words to filter the candidate set. This ensures that the selected expansion words effectively reflect the semantic and syntactic relevance of the entire query. Experimental results demonstrate that the semantic query expansion method based on Word2Vec improves both recall and precision, making it a valuable approach in the field of query expansion.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Yang and Yong Pan "Enhancing information retrieval with semantic query expansion: a Word2Vec-based approach", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701M (21 December 2023); https://doi.org/10.1117/12.3012203

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KEYWORDS

Semantics

Education and training

Tunable filters

Reflection

Complex systems

Databases

Statistical methods

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