[Purpose/significance] Based on the statistical method, the paper explored the semantic retrieval technology based on word embedding expansion to enhance the semantic retrieval ability of academic resources.[Method/process] Using Natural Language Processing and text mining technology, the paper preprocessed the collected academic resources (mainly academic papers) metadata, combined the Word2vec word embedding generation tool and the elasticsearch full text retrieval engine to build semantic retrieval system, and explored the semantic retrieval of academic resources.[Result/conclusion] The method proposed in this paper can effectively improve the retrieval effect of academic information, and it realizes the semantic retrieval of academic resources to a certain extent, and could provide reference for further research on the follow-up semantic retrieval.
Wang Renwu
,
Chen Chuanbao
,
Meng Xianru
. Semantic Retrieval Technology of Academic Resources Based on Word Embedding Extension[J]. Library and Information Service, 2018
, 62(19)
: 111
-119
.
DOI: 10.13266/j.issn.0252-3116.2018.19.014
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