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1
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 69-81 (2021) (2021)
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2
Few-shot text classification with distributional signatures
Wu, Menghua,M. Eng.Massachusetts Institute of Technology.. - : Massachusetts Institute of Technology, 2020
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3
Typology-aware neural dependency parsing : challenges and directions
Fisch, Adam(Adam Joshua). - : Massachusetts Institute of Technology, 2020
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4
Language style transfer
Shen, Tianxiao. - : Massachusetts Institute of Technology, 2018
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5
Transfer learning for low-resource natural language analysis
Zhang, Yuan, Ph. D. Massachusetts Institute of Technology. - : Massachusetts Institute of Technology, 2017
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6
Hierarchical low-rank tensors for multilingual transfer parsing
In: http://aclweb.org/anthology/D/D15/D15-1213.pdf (2015)
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7
Context-dependent type-level models for unsupervised morpho-syntactic induction
Lee, Yoong Keok. - : Massachusetts Institute of Technology, 2015
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8
Linguistically Motivated Models for Lightly-Supervised Dependency Parsing
In: http://people.csail.mit.edu/tahira/main.pdf (2014)
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9
Low-rank tensors for scoring dependency structures
In: http://people.csail.mit.edu/tommi/papers/Lei-ACL14.pdf (2014)
Abstract: Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing typically requires rich, high-dimensional feature representations. A small subset of such features is often se-lected manually. This is problematic when features lack clear linguistic meaning as in embeddings or when the information is blended across features. In this paper, we use tensors to map high-dimensional fea-ture vectors into low dimensional repre-sentations. We explicitly maintain the pa-rameters as a low-rank tensor to obtain low dimensional representations of words in their syntactic roles, and to leverage mod-ularity in the tensor for easy training with online algorithms. Our parser consistently outperforms the Turbo and MST parsers across 14 different languages. We also ob-tain the best published UAS results on 5 languages.1 1
URL: http://people.csail.mit.edu/tommi/papers/Lei-ACL14.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.647.396
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10
The MIT Faculty has made this article openly available. Please share how this access benefits you
In: http://dspace.mit.edu/openaccess-disseminate/1721.1/59314/ (2014)
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11
Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches
In: http://dspace.mit.edu/openaccess-disseminate/1721.1/62804/ (2014)
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12
Linguistically motivated models for lightly-supervised dependency parsing
Naseem, Tahira. - : Massachusetts Institute of Technology, 2014
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13
Morphological segmentation : an unsupervised method and application to Keyword Spotting ; Unsupervised method and application to KWS
Narasimhan, Karthik Rajagopal. - : Massachusetts Institute of Technology, 2014
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14
Parsing with sparse annotated resources
Zhang, Yuan, Ph. D. Massachusetts Institute of Technology. - : Massachusetts Institute of Technology, 2013
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15
Learning to map into a universal pos tagset
In: http://people.csail.mit.edu/yuanzh/papers/emnlp2012.pdf (2012)
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16
Grounding Linguistic Analysis in Control Applications
In: http://people.csail.mit.edu/branavan/papers/branavan-thesis.pdf (2012)
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17
Grounding linguistic analysis in control applications
Branavan, Satchuthananthavale Rasiah Kuhan. - : Massachusetts Institute of Technology, 2012
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18
In-domain relation discovery with meta-constraints via posterior regularization
In: http://people.csail.mit.edu/regina/my_papers/sem_acl2011.pdf (2011)
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19
Learning to win by reading manuals in a monte-carlo framework
In: http://www.aclweb.org/anthology/P11-1028/ (2011)
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20
Non-linear monte-carlo search in civilization II
In: http://people.csail.mit.edu/branavan/papers/ijcai2011.pdf (2011)
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