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This article discusses the extraction of such semantically related entities (represented by domain terms) from biomedical literature. The method combines various text-based aspects, such as lexical, syntactic, and contextual similarities between terms. Lexical similarities are based on the level of sharing of word constituents. Syntactic similarities rely on expressions (such as term enumerations and conjunctions) in which a sequence of terms appears as a single syntactic unit. Finally, contextual similarities are based on automatic discovery of relevant contexts shared among terms. The approach is evaluated using the Genia resources, and the results of experiments are presented. Lexical and syntactic links have shown high precision and low recall, while contextual similarities have resulted in significantly higher recall with moderate precision. By combining the three metrics, we achieved F measures of 68% for\n            <jats:italic>semantically related<\/jats:italic>\n            terms and 37% for\n            <jats:italic>highly related<\/jats:italic>\n            entities.\n          <\/jats:p>","DOI":"10.1145\/1131348.1131351","type":"journal-article","created":{"date-parts":[[2006,7,25]],"date-time":"2006-07-25T14:14:26Z","timestamp":1153836866000},"page":"22-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Mining semantically related terms from biomedical literature"],"prefix":"10.1145","volume":"5","author":[{"given":"Goran","family":"Nenadi\u0107","sequence":"first","affiliation":[{"name":"University of Manchester and National Centre for Text Mining, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophia","family":"Ananiadou","sequence":"additional","affiliation":[{"name":"University of Manchester and National Centre for Text Mining, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2006,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of COLING 94","author":"Ananiadou S.","year":"1994","unstructured":"Ananiadou , S. 1994 . A methodology for automatic term recognition . In Proceedings of COLING 94 , Kyoto, Japan. 1034--1038. 10.3115\/991250.991317 Ananiadou, S. 1994. A methodology for automatic term recognition. In Proceedings of COLING 94, Kyoto, Japan. 1034--1038. 10.3115\/991250.991317"},{"key":"e_1_2_1_2_1","first-page":"67","volume-title":"Artech House Books","author":"Ananiadou S.","unstructured":"Ananiadou , S. and Nenadi\u0107 , G . 2006. Automatic Terminology Management in Biomedicine. In S. Ananiadou, J. McNaught (Eds.), Text Mining for Biology and Biomedicine , Artech House Books , pp. 67 -- 98 . Ananiadou, S. and Nenadi\u0107, G. 2006. Automatic Terminology Management in Biomedicine. In S. Ananiadou, J. McNaught (Eds.), Text Mining for Biology and Biomedicine, Artech House Books, pp. 67--98."},{"key":"e_1_2_1_3_1","first-page":"25","article-title":"Automatic annotation for biological sequences by extraction of keywords from Medline abstracts. 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Genome Research Apr; 13 4 662--672.  Camon E. Magrane M. Barrell D. Binns D. Fleischmann W. Kersey P. Mulder N. Oinn T. Maslen J. Cox A. and Apweiler R. 2003. The Gene Ontology Annotation (GOA) project: implementation of GO in Swiss-Prot TrEMBL and InterPro. Genome Research Apr; 13 4 662--672.","DOI":"10.1101\/gr.461403"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"S17","DOI":"10.1186\/1471-2105-6-S1-S17","article-title":"An evaluation of GO annotation retrieval for BioCreAtIvE and GOA","volume":"6","author":"Camon E.","year":"2005","unstructured":"Camon , E. , Barrell , D. , Dimmer , E. , Lee , V. , Magrane , M. , Maslen , J. , Binns , D. , and Apweiler , R. 2005 . An evaluation of GO annotation retrieval for BioCreAtIvE and GOA . BMC Bioinformatics 6, (Suppl 1 ), S17 . Camon, E., Barrell, D., Dimmer, E., Lee, V., Magrane, M., Maslen, J., Binns, D., and Apweiler, R. 2005. An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. 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