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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...7 Hits 1 – 20 of 133 1 Deciphering Undersegmented Ancient Scripts Using Phonetic Prior Jiaming Luo; Frederik Hartmann; Enrico Santus... In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 69-81 (2021) (2021) BASE Show details 2 Few-shot text classification with distributional signatures Wu, Menghua,M. Eng.Massachusetts Institute of Technology.. - : Massachusetts Institute of Technology, 2020 BASE Show details 3 Typology-aware neural dependency parsing : challenges and directions Fisch, Adam(Adam Joshua). - : Massachusetts Institute of Technology, 2020 BASE Show details 4 Language style transfer Shen, Tianxiao. - : Massachusetts Institute of Technology, 2018 BASE Show details 5 Transfer learning for low-resource natural language analysis Zhang, Yuan, Ph. D. Massachusetts Institute of Technology. - : Massachusetts Institute of Technology, 2017 BASE Show details 6 Hierarchical low-rank tensors for multilingual transfer parsing Yuan Zhang; Regina Barzilay In: http://aclweb.org/anthology/D/D15/D15-1213.pdf (2015) BASE Show details 7 Context-dependent type-level models for unsupervised morpho-syntactic induction Lee, Yoong Keok. - : Massachusetts Institute of Technology, 2015 BASE Show details 8 Linguistically Motivated Models for Lightly-Supervised Dependency Parsing Tahira Naseem; Regina Barzilay; Leslie A. Kolodziejski In: http://people.csail.mit.edu/tahira/main.pdf (2014) BASE Show details 9 Low-rank tensors for scoring dependency structures Tao Lei; Yu Xin; Yuan Zhang... In: http://people.csail.mit.edu/tommi/papers/Lei-ACL14.pdf (2014) BASE Show details 10 The MIT Faculty has made this article openly available. Please share how this access benefits you Citable Link; Unsupervised Multilingual; Grammar Induction... In: http://dspace.mit.edu/openaccess-disseminate/1721.1/59314/ (2014) BASE Show details 11 Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches Citable Link; Tahira Naseem; Benjamin Snyder... In: http://dspace.mit.edu/openaccess-disseminate/1721.1/62804/ (2014) BASE Show details 12 Linguistically motivated models for lightly-supervised dependency parsing Naseem, Tahira. - : Massachusetts Institute of Technology, 2014 BASE Show details 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 BASE Show details 14 Parsing with sparse annotated resources Zhang, Yuan, Ph. D. Massachusetts Institute of Technology. - : Massachusetts Institute of Technology, 2013 BASE Show details 15 Learning to map into a universal pos tagset Yuan Zhang; Roi Reichart; Regina Barzilay... In: http://people.csail.mit.edu/yuanzh/papers/emnlp2012.pdf (2012) BASE Show details 16 Grounding Linguistic Analysis in Control Applications Satchuthananthavale Rasiah; Kuhan Branavan; Regina Barzilay In: http://people.csail.mit.edu/branavan/papers/branavan-thesis.pdf (2012) BASE Show details 17 Grounding linguistic analysis in control applications Branavan, Satchuthananthavale Rasiah Kuhan. - : Massachusetts Institute of Technology, 2012 BASE Show details 18 In-domain relation discovery with meta-constraints via posterior regularization Harr Chen; Edward Benson; Tahira Naseem; Regina Barzilay In: http://people.csail.mit.edu/regina/my_papers/sem_acl2011.pdf (2011) Abstract: We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context. We capture these regularities via the structure of our probabilistic model as well as a set of declaratively-specified constraints enforced during posterior inference. Across two domains our approach successfully recovers hidden relation structure, comparable to or outperforming previous state-of-the-art approaches. Furthermore, we find that a small set of constraints is applicable across the domains, and that using domain-specific constraints can further improve performance. 1 1 URL: http://people.csail.mit.edu/regina/my_papers/sem_acl2011.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.360.881 BASE Hide details 19 Learning to win by reading manuals in a monte-carlo framework S. R. K. Branavan; David Silver; Regina Barzilay In: http://www.aclweb.org/anthology/P11-1028/ (2011) BASE Show details 20 Non-linear monte-carlo search in civilization II S. R. K. Branavan; David Silver; Regina Barzilay In: http://people.csail.mit.edu/branavan/papers/ijcai2011.pdf (2011) BASE Show details Page: 1 2 3 4 5...7

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