Unsupervised multilingual learning
Name
711173292-MIT.pdf
Description
Full printable version
Size
17.71 MB
Format
Adobe PDF
Checksum (MD5)
48ff3e853d843eda0ad130a17701ed23
Author(s)
Snyder, Benjamin, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Regina Barzilay.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
For centuries, scholars have explored the deep links among human languages. In this thesis, we present a class of probabilistic models that exploit these links as a form of naturally occurring supervision. These models allow us to substantially improve performance for core text processing tasks, such as morphological segmentation, part-of-speech tagging, and syntactic parsing. Besides these traditional NLP tasks, we also present a multilingual model for lost language deciphersment. We test this model on the ancient Ugaritic language. Our results show that we can automatically uncover much of the historical relationship between Ugaritic and Biblical Hebrew, a known related language.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 241-254).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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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.
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