Difference between revisions of "Lin and Wu. 2009. Phrase Clustering for Discriminative Learning."
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{{MyCiteconference | booktitle = Proceedings of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing| coauthors = X. Wu| date = 2009| first = D.| last = Lin| title = Phrase clustering for discriminative learning| url = http://www.aclweb.org/anthology/P/P09/P09-1116.pdf }} | {{MyCiteconference | booktitle = Proceedings of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing| coauthors = X. Wu| date = 2009| first = D.| last = Lin| title = Phrase clustering for discriminative learning| url = http://www.aclweb.org/anthology/P/P09/P09-1116.pdf }} | ||
This [[Category::Paper]] is available online [http://www.aclweb.org/anthology/P/P09/P09-1116.pdf]. | This [[Category::Paper]] is available online [http://www.aclweb.org/anthology/P/P09/P09-1116.pdf]. | ||
| − | == | + | == Summary == |
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| + | This paper focuses on a variant of the [[AddressesProblem::Named Entity Recognition]] problem. They present a method for identifying nested named entities using a discriminative constituency [[UsesMethod::Parsing|parser]]. | ||
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| + | == Brief description of the method == | ||
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| + | The authors model each sentence as a constituent tree. Each named entity would correspond to a phrase in the tree (i.e a subtree). A root node would connect the entire sentence. In addition, the [[POS]] tags of non-entities are also modeled. The diagram above is one such example of a "named entity tree". | ||
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| + | == Experimental Result == | ||
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| + | The authors performed experiments on the [[UsesDataset::GENIA_dataset | GENIA Corpus]], [[UsesDataset::JNLPBA]] corpus and [[UsesDataset::Ancora]]. | ||
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| + | [[Image:GENIA_results.png]] | ||
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| + | Their system achieve significant performance gains over similar flat model [[RelatedPaper::Sarawagi_and_Cohen_NIPS_2004|semi-CRF]] NER system. | ||
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| + | [[Image:JNLPBA_results.png]] | ||
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| + | [[Image:Ancora_results.png]] | ||
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| + | The author's system are generally perform better than flat models when evaluated on all the entities as compared to just on top-level entities. It demonstrates the relevance of modeling named entities hierarchy in an [[NER]] system. | ||
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| + | == Related Papers == | ||
Revision as of 21:06, 24 September 2011
Contents
Under construction
Phrase clustering for discriminative learning, by D. Lin, X. Wu. In Proceedings of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, 2009.
This Paper is available online [1].
Summary
This paper focuses on a variant of the Named Entity Recognition problem. They present a method for identifying nested named entities using a discriminative constituency parser.
Brief description of the method
The authors model each sentence as a constituent tree. Each named entity would correspond to a phrase in the tree (i.e a subtree). A root node would connect the entire sentence. In addition, the POS tags of non-entities are also modeled. The diagram above is one such example of a "named entity tree".
Experimental Result
The authors performed experiments on the GENIA Corpus, JNLPBA corpus and Ancora.
Their system achieve significant performance gains over similar flat model semi-CRF NER system.
The author's system are generally perform better than flat models when evaluated on all the entities as compared to just on top-level entities. It demonstrates the relevance of modeling named entities hierarchy in an NER system.
