{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/66413"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/66413","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Disambiguating words with self-organizing maps","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_html":"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_has_math":false,"creators":["Couturier, Martin Marcel"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Patrick H. Winston."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:20:48Z","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. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/66413","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Patrick H. Winston."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Couturier, Martin Marcel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2011-10-17T21:23:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2011-10-17T21:23:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/66413"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Disambiguating words with self-organizing maps"]}]}],"canonical_facts":{"dc:contributor.advisor":["Patrick H. Winston."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Couturier, Martin Marcel"],"dc:date.accessioned":["2011-10-17T21:23:17Z"],"dc:date.available":["2011-10-17T21:23:17Z"],"dc:date.issued":["2011"],"dc:description":["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)."],"dc:description.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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/66413"],"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. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Disambiguating words with self-organizing maps"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:48Z"}