Empirical distributional semantics: methods and biomedical applications. Export

@article{citeulike:4097812, abstract = {Over the past 15 years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness from free text, as well as their evaluation in a variety of biomedical and other applications. Recent methodological developments, and their applicability to several existing applications are also discussed.}, address = {San Diego, USA}, author = {Cohen, Trevor and Widdows, Dominic}, citeulike-article-id = {4097812}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1518431}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.jbi.2009.02.002}, citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S1532046409000227}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19232399}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19232399}, day = {13}, doi = {10.1016/j.jbi.2009.02.002}, issn = {1532-0480}, journal = {Journal of biomedical informatics}, keywords = {bioinformatics, distributional-similarity, lda, lsa, plsa, topic-models}, month = {April}, number = {2}, pages = {390--405}, posted-at = {2009-09-10 15:04:19}, priority = {5}, publisher = {Elsevier Science}, title = {Empirical distributional semantics: methods and biomedical applications.}, url = {http://dx.doi.org/10.1016/j.jbi.2009.02.002}, volume = {42}, year = {2009} }

TY - JOUR ID - citeulike:4097812 L3 - citeulike-article-id:4097812 N2 - Over the past 15 years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness from free text, as well as their evaluation in a variety of biomedical and other applications. Recent methodological developments, and their applicability to several existing applications are also discussed. IS - 2 JF - Journal of biomedical informatics SN - 1532-0480 AD - San Diego, USA EP - 405 TI - Empirical distributional semantics: methods and biomedical applications. PB - Elsevier Science VL - 42 SP - 390 KW - bioinformatics KW - distributional-similarity KW - lda KW - lsa KW - plsa KW - topic-models AU - Cohen, Trevor AU - Widdows, Dominic PY - 2009/04/13/ UR - http://dx.doi.org/10.1016/j.jbi.2009.02.002 ER -