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Hits 41 – 60 of 113

41
Improving Coreference Resolution by Learning Entity-Level Distributed Representations ...
BASE
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42
The marionette strings ...
Manning, Christopher. - : University of Southern California Digital Library (USC.DL), 2015
BASE
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43
Universal Dependencies 1.1
Agić, Željko; Aranzabe, Maria Jesus; Atutxa, Aitziber. - : Universal Dependencies Consortium, 2015
BASE
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44
Universal Dependencies 1.2
Nivre, Joakim; Agić, Željko; Aranzabe, Maria Jesus. - : Universal Dependencies Consortium, 2015
BASE
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45
Universal Dependencies 1.0
Nivre, Joakim; Bosco, Cristina; Choi, Jinho. - : Universal Dependencies Consortium, 2015
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46
A large annotated corpus for learning natural language inference ...
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47
Text to 3D Scene Generation with Rich Lexical Grounding ...
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48
Natural Language Processing: Integration of Automatic and Manual Analysis
Eckart de Castilho, Richard [Verfasser]; Gurevych, Iryna [Akademischer Betreuer]; Henrich, Andreas [Akademischer Betreuer]. - Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2014
DNB Subject Category Language
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49
Learning Distributed Word Representations for Natural Logic Reasoning ...
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50
Discovery of Deep Structure from Unlabeled Data
In: DTIC (2014)
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51
Induced lexico-syntactic patterns improve information extraction from online medical forums
Gupta, Sonal; MacLean, Diana L; Heer, Jeffrey. - : BMJ Publishing Group, 2014
BASE
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52
Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning ...
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53
Faust: Flexible Acquistion and Understanding System for Text
In: DTIC (2013)
BASE
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54
Measuring machine translation quality as semantic equivalence: a metric based on entailment features
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 23 (2010) 2-3, 181-193
BLLDB
OLC Linguistik
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55
Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates
In: Speech communication. - Amsterdam [u.a.] : Elsevier 52 (2010) 3, 181-200
BLLDB
OLC Linguistik
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56
Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates
In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-00608401 ; Speech Communication, Elsevier : North-Holland, 2010, 52 (3), pp.181. ⟨10.1016/j.specom.2009.10.001⟩ (2010)
BASE
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57
Hierarchical joint learning: improving joint parsing and named entity recognition with non-jointly labeled data
In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 720-728
BLLDB
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58
"Was it good? It was provocative.": Learning the meaning of scalar adjectives
In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 166-176
BLLDB
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59
A global joint model for semantic role labeling
In: Computational linguistics. - Cambridge, Mass. : MIT Press 34 (2008) 2, 161-191
BLLDB
OLC Linguistik
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60
Introduction to information retrieval
Schütze, Hinrich; Manning, Christopher D.; Raghavan, Prabhakar. - Cambridge [u.a.] : Cambridge Univ. Press, 2008
BLLDB
UB Frankfurt Linguistik
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