2026-09-18T23:59:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/331112022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter Svolovits.
Bhooshan, Neha
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-06-19T17:41:37Z
2006-06-19T17:41:37Z
2005
2005
http://hdl.handle.net/1721.1/33111
62232819
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaf 38).
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.
by Neha Bhooshan.
M.Eng.
39 leaves
2414432 bytes
2414074 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Classification of semantic relations in different syntactic structures in medical text using the MeSH hierarchy
Thesis
application/pdf
Publication
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unknown
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<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>
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