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Structured generative models for unsupervised named-entity clustering
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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)
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Keyword:
080100 Artificial Intelligence and Image Processing
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URL: http://hdl.handle.net/1959.14/323580
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Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
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Sentence-Internal Prosody Does not Help Parsing the Way Punctuation Does
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Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon construction ...
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