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Improving semantic similarity retrieval with word embeddings
Author(s) -
Yan Fengqi,
Fan Qiaoqing,
Lu Mingming
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4489
Subject(s) - word2vec , computer science , semantic similarity , similarity (geometry) , word (group theory) , synonym (taxonomy) , information retrieval , artificial intelligence , natural language processing , semantic gap , image retrieval , mathematics , botany , geometry , embedding , biology , image (mathematics) , genus
Summary Word similarity matchmaking is one of the core research areas of information retrieval. The existing methods based on a synonym dictionary would lead to the problem of semantic gap, which could be caused by the absence of synonyms. To address this problem, we improve semantic similarity retrieval by incorporating word embeddings. Especially, word embeddings are trained by Word2Vec and then use them to depict the semantic similarity between words. Experiments are conducted on two different datasets, ie, one is a public long text dataset (ie, Reuters‐21578), and the other is a short text dataset (ie, 120ask) collected from a healthcare community. The experimental results on the two datasets show that the proposed method further improves the accuracy of the similarity retrieval.