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Identifying synonymy between relational phrases using word embeddings
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NSTL
Elsevier
Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Most of the previous work that has addressed this task of synonymy resolution uses similarity metrics between relational phrases based on textual strings or dependency paths, which, for the most part, ignore the context around the relations. To overcome this shortcoming, we employ a word embedding technique to encode relational phrases. We then apply the k-means algorithm on top of the distributional representations to cluster the phrases. Our experimental results show that this approach outperforms state-of-the-art statistical models including latent Dirichlet allocation and Markov logic networks. (C) 2015 Elsevier Inc. All rights reserved.
Word embeddingsSynonym resolutionRelational phrase clusteringTopic modeling
Nguyen, Nhung T. H.、Miwa, Makoto、Tsuruoka, Yoshimasa、Tojo, Satoshi
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Vietnam Natl Univ, Univ Sci, Ho Chi Minh City, Vietnam
Toyota Technol Inst, Tempaku Ku, Nagoya, Aichi 4688511, Japan
Univ Tokyo, Bunkyo Ku, Tokyo 1138656, Japan
Japan Adv Inst Sci & Technol, Nomi, Ishikawa 9231292, Japan
NSTL
Elsevier
