Decoding algorithms for complex natural language tasks
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/41647 |
| _version_ | 1826217252407476224 |
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| author | Deshpande, Pawan |
| author2 | Regina Barzilay. |
| author_facet | Regina Barzilay. Deshpande, Pawan |
| author_sort | Deshpande, Pawan |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. |
| first_indexed | 2024-09-23T17:00:25Z |
| format | Thesis |
| id | mit-1721.1/41647 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:00:25Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/416472019-04-12T16:08:26Z Decoding algorithms for complex natural language tasks Deshpande, Pawan Regina Barzilay. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. Includes bibliographical references (p. 79-85). 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. by Pawan Deshpande. M.Eng. 2008-05-19T16:05:01Z 2008-05-19T16:05:01Z 2007 2007 Thesis http://hdl.handle.net/1721.1/41647 219709911 eng 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 85 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Deshpande, Pawan Decoding algorithms for complex natural language tasks |
| title | Decoding algorithms for complex natural language tasks |
| title_full | Decoding algorithms for complex natural language tasks |
| title_fullStr | Decoding algorithms for complex natural language tasks |
| title_full_unstemmed | Decoding algorithms for complex natural language tasks |
| title_short | Decoding algorithms for complex natural language tasks |
| title_sort | decoding algorithms for complex natural language tasks |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/41647 |
| work_keys_str_mv | AT deshpandepawan decodingalgorithmsforcomplexnaturallanguagetasks |