Learning precise partial semantic mappings via linear algebra
Name
965386321-MIT.pdf
Description
Full printable version
Size
2.51 MB
Format
Adobe PDF
Checksum (MD5)
2c6882a3d45c0c04ae398c43f66be98b
Author(s)
Khani, Fereshte
Advisor(s)
Martin Rinard.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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