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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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| 2 |
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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| 3 |
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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| 4 |
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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| 5 |
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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| 6 |
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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| 7 |
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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| 8 |
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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| 9 |
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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| 10 |
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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| 11 |
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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| 12 |
Learning to win by reading manuals in a monte-carlo framework
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In: http://people.csail.mit.edu/branavan/papers/acl2011.pdf (2011)
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| 13 |
Non-linear monte-carlo search in civilization II
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In: http://people.csail.mit.edu/regina/my_papers/civ_ijcai2011.pdf (2011)
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| 14 |
Learning to win by reading manuals in a monte-carlo framework
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In: http://people.csail.mit.edu/regina/my_papers/civ11.pdf (2011)
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| 15 |
Learning to win by reading manuals in a monte-carlo framework
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In: http://jair.org/media/3484/live-3484-6254-jair.pdf (2011)
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| 16 |
Learning Semantic Structures from In-domain Documents
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In: http://people.csail.mit.edu/harr/harr_thesis.pdf (2010)
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Abstract:
Semantic analysis is a core area of natural language understanding that has typically focused on predicting domain-independent representations. However, such representations are unable to fully realize the rich diversity of technical content prevalent in a variety of specialized domains. Taking the standard supervised approach to domainspecific semantic analysis requires expensive annotation effort for each new domain of interest. In this thesis, we study how multiple granularities of semantic analysis can be learned from unlabeled documents within the same domain. By exploiting indomain regularities in the expression of text at various layers of linguistic phenomena, including lexicography, syntax, and discourse, the statistical approaches we propose induce multiple kinds of structure: relations at the phrase and sentence level, content models at the paragraph and section level, and semantic properties at the document level. Each of our models is formulated in a hierarchical Bayesian framework with the target structure captured as latent variables, allowing them to seamlessly incorporate
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URL: http://people.csail.mit.edu/harr/harr_thesis.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.2604
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| 17 |
Climbing the tower of Babel: Unsupervised multilingual learning
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In: http://pages.cs.wisc.edu/~bsnyder/papers/bsnyder_icml2010.pdf (2010)
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| 18 |
Unsupervised Multilingual Learning
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In: http://pages.cs.wisc.edu/~bsnyder/papers/bsnyder-thesis.pdf (2010)
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| 19 |
Unsupervised multilingual grammar induction
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In: http://people.csail.mit.edu/tahira/acl09.pdf (2009)
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| 20 |
Unsupervised multilingual grammar induction
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In: http://www.mt-archive.info/ACL-2009-Snyder.pdf (2009)
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