Improving information extraction by acquiring external evidence with reinforcement learning
Name
1020179766-MIT.pdf
Description
Full printable version
Size
359.88 KB
Format
Adobe PDF
Checksum (MD5)
40d7b8b2d32e78c6f8364546e9e62d18
Author(s)
Yala, Adam
Advisor(s)
Regina Barzilay.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
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
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
