Abstract
We present an ad hoc concept modeling approach using distributional semantic models to identify fine-grained entities and their relations in an online search setting. Concepts are generated from user-defined seed terms, distributional evidence, and a relational model over concept distributions. A dimensional indexing model is used for efficient aggregation of distributional, syntactic, and relational evidence. The proposed semi-supervised model allows concepts to be defined and related at varying levels of granularity and scope. Qualitative evaluations on medical records, intelligence documents, and open domain web data demonstrate the efficacy of our approach.
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CITATION STYLE
Urbain, J., Bushee, G., Knudson, P., Kowalski, G., & Taylor, B. (2015). Distributional semantic concept models for entity relation discovery. In 1st Workshop on Vector Space Modeling for Natural Language Processing, VS 2015 at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 (pp. 49–55). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w15-1507
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