Learning to answer questions from semi-structured knowledge sources
Name
965624572-MIT.pdf
Description
Full printable version
Size
2.31 MB
Format
Adobe PDF
Checksum (MD5)
21864a32f2c39f0178f236046348b23d
Author(s)
Morales, Alvaro, M. Eng. Massachusetts Institute of Technology
Advisor(s)
Boris Katz.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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