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Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 69-81 (2021) (2021)
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Typology-aware neural dependency parsing : challenges and directions
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Transfer learning for low-resource natural language analysis
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Hierarchical low-rank tensors for multilingual transfer parsing
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In: http://aclweb.org/anthology/D/D15/D15-1213.pdf (2015)
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Context-dependent type-level models for unsupervised morpho-syntactic induction
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Abstract:
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). ; 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. ; by Yoong Keok Lee. ; Ph. D.
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Keyword:
Electrical Engineering and Computer Science
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URL: http://hdl.handle.net/1721.1/97759
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Linguistically Motivated Models for Lightly-Supervised Dependency Parsing
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In: http://people.csail.mit.edu/tahira/main.pdf (2014)
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Low-rank tensors for scoring dependency structures
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In: http://people.csail.mit.edu/tommi/papers/Lei-ACL14.pdf (2014)
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The MIT Faculty has made this article openly available. Please share how this access benefits you
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In: http://dspace.mit.edu/openaccess-disseminate/1721.1/59314/ (2014)
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Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches
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In: http://dspace.mit.edu/openaccess-disseminate/1721.1/62804/ (2014)
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Linguistically motivated models for lightly-supervised dependency parsing
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Morphological segmentation : an unsupervised method and application to Keyword Spotting ; Unsupervised method and application to KWS
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Learning to map into a universal pos tagset
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In: http://people.csail.mit.edu/yuanzh/papers/emnlp2012.pdf (2012)
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Grounding Linguistic Analysis in Control Applications
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In: http://people.csail.mit.edu/branavan/papers/branavan-thesis.pdf (2012)
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In-domain relation discovery with meta-constraints via posterior regularization
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In: http://people.csail.mit.edu/regina/my_papers/sem_acl2011.pdf (2011)
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Learning to win by reading manuals in a monte-carlo framework
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In: http://www.aclweb.org/anthology/P11-1028/ (2011)
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Non-linear monte-carlo search in civilization II
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In: http://people.csail.mit.edu/branavan/papers/ijcai2011.pdf (2011)
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