{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/33111"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/33111","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Classification of semantic relations in different syntactic structures in medical text using the MeSH hierarchy","abstract":"Two different classification algorithms are evaluated in recognizing semantic relationships of different syntactic compounds. The compounds, which include noun- noun, adjective-noun, noun-adjective, noun-verb, and verb-noun, were extracted from a set of doctors' notes using a part of speech tagger and a parser. Each compound was labeled with a semantic relationship, and each word in the compound was mapped to its corresponding entry in the MeSH hierarchy. MeSH includes only medical terminology so it was extended to include everyday, non-medical terms. The two classification algorithms, neural networks and a classification tree, were trained and tested on the data set for each type of syntactic compound. Models representing different levels of MeSH were generated and fed into the neural networks. Both algorithms performed better than random guessing, and the classification tree performed better than the neural networks in predicting the semantic relationship between phrases from their syntactic structure.","abstract_html":"Two different classification algorithms are evaluated in recognizing semantic relationships of different syntactic compounds. The compounds, which include noun- noun, adjective-noun, noun-adjective, noun-verb, and verb-noun, were extracted from a set of doctors' notes using a part of speech tagger and a parser. Each compound was labeled with a semantic relationship, and each word in the compound was mapped to its corresponding entry in the MeSH hierarchy. MeSH includes only medical terminology so it was extended to include everyday, non-medical terms. The two classification algorithms, neural networks and a classification tree, were trained and tested on the data set for each type of syntactic compound. Models representing different levels of MeSH were generated and fed into the neural networks. Both algorithms performed better than random guessing, and the classification tree performed better than the neural networks in predicting the semantic relationship between phrases from their syntactic structure.","abstract_has_math":false,"creators":["Bhooshan, Neha"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Peter Svolovits."],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005","date_published":"2005","updated_at":"2026-07-22T22:22:18Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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The compounds, which include noun- noun, adjective-noun, noun-adjective, noun-verb, and verb-noun, were extracted from a set of doctors' notes using a part of speech tagger and a parser. Each compound was labeled with a semantic relationship, and each word in the compound was mapped to its corresponding entry in the MeSH hierarchy. MeSH includes only medical terminology so it was extended to include everyday, non-medical terms. The two classification algorithms, neural networks and a classification tree, were trained and tested on the data set for each type of syntactic compound. Models representing different levels of MeSH were generated and fed into the neural networks. 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The two classification algorithms, neural networks and a classification tree, were trained and tested on the data set for each type of syntactic compound. Models representing different levels of MeSH were generated and fed into the neural networks. Both algorithms performed better than random guessing, and the classification tree performed better than the neural networks in predicting the semantic relationship between phrases from their syntactic structure."],"dc:description.degree":["M.Eng."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/33111"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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