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1
Automatic domain adaptation for parsing
McClosky, David; Charniak, Eugene; Johnson, Mark. - : Stroudsburg, PA : Association for Computational Linguistics, 2010
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2
Structured generative models for unsupervised named-entity clustering
Elsner, Micha; Charniak, Eugene; Johnson, Mark. - : East Stroudsburg, PA : Association for Computational Linguistics, 2009
Abstract: We describe a generative model for clustering named entities which also models named entity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference information from an unsupervised pronoun resolver. The model scores 86% on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsupervised model, and the best score for a generative model. ; 9 page(s)
Keyword: 080100 Artificial Intelligence and Image Processing
URL: http://hdl.handle.net/1959.14/323580
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3
When is self-training effective for parsing?
McClosky, David; Charniak, Eugene; Johnson, Mark. - : Stroudsburg, Pa : Association for Computational Linguistics, 2008
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4
Effective Self-Training for Parsing
McClosky, David; Charniak, Eugene; Johnson, Mark. - : East Stroudsburg, PA : ACL, 2006
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5
Effective Use of Prosody in Parsing Conversational Speech
Kahn, J; Lease, Matthew; Charniak, Eugene. - : East Stroudsburg, PA : Association for Computational Linguistics, 2005
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6
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
Charniak, Eugene; Johnson, Mark. - : East Stroudsburg, PA : Association for Computational Linguistics, 2005
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7
A TAG-based noisy channel model of speech repairs
Johnson, Mark; Charniak, Eugene. - : East Stroudsburg, PA : Association for Computational Linguistics, 2004
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8
Sentence-Internal Prosody Does not Help Parsing the Way Punctuation Does
Gregory, M; Johnson, Mark; Charniak, Eugene. - : East Stroudsburg, PA : Association for Computational Linguistics, 2004
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