2026-09-20T05:11:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/664132022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick H. Winston.
Couturier, Martin Marcel
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-10-17T21:23:17Z
2011-10-17T21:23:17Z
2011
2011
http://hdl.handle.net/1721.1/66413
755091036
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 77).
Today, powerful programs readily parse English text; understanding, however, is another matter. In this thesis, I take a step toward understanding by introducing CLARIFY, a program that disambiguates words. CLARIFY identifies patterns in observed word contexts, and uses these patterns to select the optimal word sense for any specific situation. CLARIFY learns successful patterns by manipulating an accelerated Self-Organizing Map to save these example contexts and then references them to perform further context based disambiguation within the language. Through this process and after training on 125 examples, CLARIFY can now decipher that shrimp in the sentence "The shrimp goes to the store. " is a small-person, not relying on a literal definition of each word as a separate element but looking at the sentence as a fluid solution of many elements, thereby making the inference crustacean absurd. CLARIFY is implemented in 1500 lines of Java.
by Martin Marcel Couturier.
M.Eng.
77 p.
eng
Massachusetts Institute of Technology
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
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Disambiguating words with self-organizing maps
Thesis
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<Abstract>Today, powerful programs readily parse English text; understanding, however, is another matter. In this thesis, I take a step toward understanding by introducing CLARIFY, a program that disambiguates words. CLARIFY identifies patterns in observed word contexts, and uses these patterns to select the optimal word sense for any specific situation. CLARIFY learns successful patterns by manipulating an accelerated Self-Organizing Map to save these example contexts and then references them to perform further context based disambiguation within the language. Through this process and after training on 125 examples, CLARIFY can now decipher that shrimp in the sentence "The shrimp goes to the store. " is a small-person, not relying on a literal definition of each word as a separate element but looking at the sentence as a fluid solution of many elements, thereby making the inference crustacean absurd. CLARIFY is implemented in 1500 lines of Java.</Abstract>
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