title: Identifying synonymy between relational phrases using word embeddings.
abstract: 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.
authors.affiliation: University of Science, Vietnam National University, Ho Chi Minh City, 227 Nguyen Van Cu St., Ward 4, Dist. 5, Ho Chi Minh City, Viet Nam; Japan Advanced Institute of Science and Technology, 1-8 Asahidai, Nomi-shi, Ishikawa 923-1292, Japan. Electronic address: nthnhung@jaist.ac.jp.
authors.forename: Nhung T H
authors.initials: NT
authors.lastname: Nguyen
authors.affiliation: Toyota Technological Institute, 2-12-1 Hisakata, Tempaku-ku, Nagoya 468-8511, Japan. Electronic address: makoto-miwa@toyota-ti.ac.jp.
authors.forename: Makoto
authors.initials: M
authors.lastname: Miwa
authors.affiliation: The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. Electronic address: tsuruoka@logos.t.u-tokyo.ac.jp.
authors.forename: Yoshimasa
authors.initials: Y
authors.lastname: Tsuruoka
authors.affiliation: Japan Advanced Institute of Science and Technology, 1-8 Asahidai, Nomi-shi, Ishikawa 923-1292, Japan. Electronic address: tojo@jaist.ac.jp.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Tojo
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005189:False Positive Reactions; D017143:Fuzzy Logic; D016239:MEDLINE; D008390:Markov Chains; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D009323:Natural Language Processing; D011336:Probability; D015203:Reproducibility of Results; D012660:Semantics; D018875:Vocabulary, Controlled
keywords: Relational phrase clustering; Synonym resolution; Topic modeling; Word embeddings