Decoding algorithms for complex natural language tasks
Name
219709911-MIT.pdf
Description
Full printable version
Size
4.33 MB
Format
Adobe PDF
Checksum (MD5)
317172dbe8d955024b46a7d757375ba7
Author(s)
Deshpande, Pawan
Advisor(s)
Regina Barzilay.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
Abstract
This thesis focuses on developing decoding techniques for complex Natural Language Processing (NLP) tasks. The goal of decoding is to find an optimal or near optimal solution given a model that defines the goodness of a candidate. The task is challenging because in a typical problem the search space is large, and the dependencies between elements of the solution are complex. The goal of this work is two-fold. First, we are interested in developing decoding techniques with strong theoretical guarantees. We develop a decoding model based on the Integer Linear Programming paradigm which is guaranteed to compute the optimal solution and is capable of accounting for a wide range of global constraints. As an alternative, we also present a novel randomized algorithm which can guarantee an arbitrarily high probability of finding the optimal solution. We apply these methods to the task of constructing temporal graphs and to the task of title generation. Second, we are interested in carefully investigating the relations between learning and decoding. We build on the Perceptron framework to integrate the learning and decoding procedures into a single unified process. We use the resulting model to automatically generate tables-of-contents, structures with deep hierarchies and rich contextual dependencies. In all three natural language tasks, our experimental results demonstrate that theoretically grounded and stronger decoding strategies perform better than existing methods. As a final contribution, we have made the source code for these algorithms publicly available for the NLP research community.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 79-85).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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