2026-09-18T20:29:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/624552022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Regina Barzilay.
Snyder, Benjamin, Ph. D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-04-25T16:01:40Z
2011-04-25T16:01:40Z
2010
2010
http://hdl.handle.net/1721.1/62455
711173292
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).
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.
by Benjamin Snyder.
Ph.D.
254 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Unsupervised multilingual learning
Thesis
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