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» Exploiting Syntactic Structure for Language Modeling
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96
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ACL
2009
14 years 10 months ago
Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria
In this paper, we propose a novel method for semi-supervised learning of nonprojective log-linear dependency parsers using directly expressed linguistic prior knowledge (e.g. a no...
Gregory Druck, Gideon S. Mann, Andrew McCallum
120
Voted
EACL
2009
ACL Anthology
14 years 10 months ago
Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument S
like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level...
Stephen Wan, Mark Dras, Robert Dale, Cécile...
111
Voted
ACL
2006
15 years 2 months ago
Learning to Predict Case Markers in Japanese
Japanese case markers, which indicate the grammatical relation of the complement NP to the predicate, often pose challenges to the generation of Japanese text, be it done by a for...
Hisami Suzuki, Kristina Toutanova
ACL
2008
15 years 2 months ago
Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition
We present a novel hierarchical prior structure for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task acr...
Andrew Arnold, Ramesh Nallapati, William W. Cohen
107
Voted
ACL
1992
15 years 1 months ago
GPSM: A Generalized Probabilistic Semantic Model for Ambiguity Resolution
In natural language processing, ambiguity resolution is a central issue, and can be regarded as a preference assignment problem. In this paper, a Generalized Probabilistic Semanti...
Jing-Shin Chang, Yih-Fen Luo, Keh-Yih Su