2026-09-19T02:41:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1060992022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Martin Rinard.
Khani, Fereshte
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T16:28:57Z
2016-12-22T16:28:57Z
2016
2016
http://hdl.handle.net/1721.1/106099
965386321
Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-42).
In natural language interfaces, having high precision, i.e., abstaining when the system is unsure, is critical for good user experience. However, most NLP systems are trained to maximize accuracy with precision as an afterthought. In this thesis, we put precision first and ask: Can we learn to map parts of the sentence to logical predicates with absolute certainty? To tackle this question, we model semantic mappings from words to predicates as matrices, which allows us to reason efficiently over the entire space of semantic mappings consistent with the training data. We prove that our method obtains 100% precision. Empirically, we demonstrate the effectiveness of our approach on the GeoQuery dataset.
by Fereshte Khani.
S.M. in Computer Science and Engineering
42 pages
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 permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Learning precise partial semantic mappings via linear algebra
Thesis
application/pdf
Publication
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