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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 Abstract: Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. ; 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 (p. 67-73). ; This thesis focuses on algorithms for parsing within the context of sparse annotated resources. Despite recent progress in parsing techniques, existing methods require significant resources for training. Therefore, current technology is limited when it comes to parsing sentences in new languages or new grammars. We propose methods for parsing when annotated resources are limited. In the first scenario, we explore an automatic method for mapping language-specific part of- speech (POS) tags into a universal tagset. Universal tagsets play a crucial role in cross-lingual syntactic transfer of multilingual dependency parsers. Our central assumption is that a high-quality mapping yields POS annotations with coherent linguistic properties which are consistent across source and target languages. We encode this intuition in an objective function. Given the exponential size of the mapping space, we propose a novel method for optimizing the objective over mappings. Our results demonstrate that automatically induced mappings rival their manually designed counterparts when evaluated in the context of multilingual parsing. In the second scenario, we consider the problem of cross-formalism transfer in parsing. We are interested in parsing constituency-based grammars such as HPSG and CCG using a small amount of data annotated in the target formalisms and a large quantity of coarse CFG annotations from the Penn Treebank. While the trees annotated in all of the target formalisms share a similar basic syntactic structure with the Penn Treebank CFG, they also encode additional constraints and semantic features. To handle this apparent difference, we design a probabilistic model that jointly generates CFG and target formalism parses. The model includes features of both parses, enabling transfer between the formalisms, and preserves parsing efficiency. Experimental results show that across a range of formalisms, our model benefits from the coarse annotations. ; by Yuan Zhang. ; S.M. Keyword: Electrical Engineering and Computer Science URL: http://hdl.handle.net/1721.1/82180 BASE Hide 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... In: http://people.csail.mit.edu/regina/my_papers/sem_acl2011.pdf (2011) BASE Show 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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