Context-dependent type-level models for unsupervised morpho-syntactic induction
Name
912300731-MIT.pdf
Description
Full printable version
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1004.57 KB
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11c5a80c4aaaa3713be2b1985c437d83
Author(s)
Lee, Yoong Keok
Advisor(s)
Regina Barzilay.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
This thesis improves unsupervised methods for part-of-speech (POS) induction and morphological word segmentation by modeling linguistic phenomena previously not used. For both tasks, we realize these linguistic intuitions with Bayesian generative models that first create a latent lexicon before generating unannotated tokens in the input corpus. Our POS induction model explicitly incorporates properties of POS tags at the type-level which is not parameterized by existing token-based approaches. This enables our model to outperform previous approaches on a range of languages that exhibit substantial syntactic variation. In our morphological segmentation model, we exploit the fact that axes are correlated within a word and between adjacent words. We surpass previous unsupervised segmentation systems on the Modern Standard Arabic Treebank data set. Finally, we showcase the utility of our unsupervised segmentation model for machine translation of the Levantine dialectal Arabic for which there is no known segmenter. We demonstrate that our segmenter outperforms supervised and knowledge-based alternatives.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 126-141).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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