2026-09-19T17:50:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1134532026-06-06T00:56:27Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay.
Yala, Adam
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-02-08T15:58:21Z
2018-02-08T15:58:21Z
2017
2017
http://hdl.handle.net/1721.1/113453
1020179766
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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 33-35).
Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases - of shooting incidents, and food adulteration cases - demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline
by Adam Yala.
M.Eng.
35 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Improving information extraction by acquiring external evidence with reinforcement learning
Thesis
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