2026-09-19T17:49:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1059732026-06-06T00:49:13Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Boris Katz.
Morales, Alvaro, M. Eng. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:17:01Z
2016-12-22T15:17:01Z
2016
2016
http://hdl.handle.net/1721.1/105973
965624572
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).
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.
by Alvaro Morales.
M.Eng.
50 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 to answer questions from semi-structured knowledge sources
Thesis
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