Domain knowledge acquisition via language grounding
Name
862113579-MIT.pdf
Description
Full printable version
Size
4.57 MB
Format
Adobe PDF
Checksum (MD5)
d11489b3f2af739752f12389fd3ae01f
Author(s)
Lei, Tao, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Regina Barzilay.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
This thesis addresses the language grounding problem at the level of word relation extraction. We propose methods to acquire knowledge represented in the form of relations and utilize them in two domain applications, high-level planning in a complex virtual world and input parser generation from input format specifications. In the first application, we propose a reinforcement learning framework to jointly learn to predict precondition relations from text and to perform high-level planning guided by those relations. When applied to a complex virtual world and text describing that world, our relation extraction technique performs on par with a supervised baseline, and we show that a high-level planner utilizing these extracted relations significantly outperforms a strong, text unaware baseline. In the second application, we use a sampling framework to predict relation trees and to generate input parser code from those trees. Our results show that our approach outperforms a state-of-the-art semantic parser on a dataset of input format specifications from the ACM International Collegiate Programming Contest, which were written in English for humans with no intention of providing support for automated processing.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 54-60).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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