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Learning to answer questions from semi-structured knowledge sources

Author(s)
Morales, Alvaro, M. Eng. Massachusetts Institute of Technology
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Boris Katz.
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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
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Abstract
Question answering is an efficient and convenient way for humans to make use of the massive amount of information on the Web. I start with an interesting source of information -- infoboxes in Wikipedia that summarize factoid knowledge -- and develop a comprehensive approach to answering questions with high precision. I first build a system to access data in infoboxes in a structured manner. I use the system to construct a crowdsourced dataset of over 15,000 high-quality, diverse questions. With these questions, I train a convolutional neural network model that outperforms models that achieve top results in similar answer selection tasks.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
 
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 47-50).
 
Date issued
2016
URI
http://hdl.handle.net/1721.1/105973
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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