2026-09-19T13:42:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/283362022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Boris Katz.
Marton, Gregory A. (Gregory Adam), 1977-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T19:51:39Z
2005-09-26T19:51:39Z
2003
2004
http://hdl.handle.net/1721.1/28336
55675517
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (p. 123-129).
People's names, dates, locations, organizations, and various numeric expressions, collectively called Named Entities, are used to convey specific meanings to humans in the same way that identifiers and constants convey meaning to a computer language interpreter. Natural Language Question Answering can benefit from understanding the meaning of these expressions because answers in a text are often phrased differently from questions and from each other. For example, "9/11" might mean the same as "September 11th" and "Mayor Rudy Giuliani" might be the same person as "Rudolph Giuliani". Sepia, the system presented here, uses a lexicon of lambda expressions and a mildly context-sensitive parser to create a data structure for each named entity. The parser and grammar design are inspired by Combinatory Categorial Grammar. The data structures are designed to capture semantic dependencies using common syntactic forms. Sepia differs from other natural language parsers in that it does not use a pipeline architecture. As yet there is no statistical component in the architecture. To evaluate Sepia, I use examples tp illustrate its qualitative differences from other named entity systems, I measure component performance on Automatic Content Extraction (ACE) competition held-out training data. and I assess end-to-end performance in the Infolab's TREC-12 Question Answering competition entry. Sepia will compete in the ACE Entity Detection and Tracking track at the end of September.
by Gregory A. Marton.
S.M.
129 p.
7400780 bytes
7417300 bytes
application/pdf
application/pdf
en_US
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 permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Sepia : semantic parsing for named entities
Semantic parsing for named entities
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<Abstract>People's names, dates, locations, organizations, and various numeric expressions, collectively called Named Entities, are used to convey specific meanings to humans in the same way that identifiers and constants convey meaning to a computer language interpreter. Natural Language Question Answering can benefit from understanding the meaning of these expressions because answers in a text are often phrased differently from questions and from each other. For example, "9/11" might mean the same as "September 11th" and "Mayor Rudy Giuliani" might be the same person as "Rudolph Giuliani". Sepia, the system presented here, uses a lexicon of lambda expressions and a mildly context-sensitive parser to create a data structure for each named entity. The parser and grammar design are inspired by Combinatory Categorial Grammar. The data structures are designed to capture semantic dependencies using common syntactic forms. Sepia differs from other natural language parsers in that it does not use a pipeline architecture. As yet there is no statistical component in the architecture. To evaluate Sepia, I use examples tp illustrate its qualitative differences from other named entity systems, I measure component performance on Automatic Content Extraction (ACE) competition held-out training data. and I assess end-to-end performance in the Infolab's TREC-12 Question Answering competition entry. Sepia will compete in the ACE Entity Detection and Tracking track at the end of September.</Abstract>
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