Difference between revisions of "Lin and Wu. 2009. Phrase Clustering for Discriminative Learning."
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== Summary == | == Summary == | ||
| − | This paper | + | This paper makes use of phrase clustering to improve on the state of the art for the [[AddressesProblem::Named Entity Recognition]] problem. They obtained 1 F-score improvement over NER systems on the CoNLL benchmark (in 2009). In their paper, phrases are basically queries that occur more than 100 times in a 700 billion token web corpus ([[RelatedPaper:Lin et al., 2008]]). |
== Brief description of the method == | == Brief description of the method == | ||
| − | + | Due to the large number of possible phrases, the authors used Bloom filters to decide whether a sequence of tokens is considered a phrase. | |
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| + | === Phrases as feature vectors === | ||
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| + | Each phrase is represented as a vector of its context. The frequency count of words appearing within a fixed sized window is aggregated and converted into [[UsesMethod::pointwise mutual information]](PMI) values | ||
Revision as of 21:58, 24 September 2011
Contents
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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 makes use of phrase clustering to improve on the state of the art for the Named Entity Recognition problem. They obtained 1 F-score improvement over NER systems on the CoNLL benchmark (in 2009). In their paper, phrases are basically queries that occur more than 100 times in a 700 billion token web corpus (RelatedPaper:Lin et al., 2008).
Brief description of the method
Due to the large number of possible phrases, the authors used Bloom filters to decide whether a sequence of tokens is considered a phrase.
Phrases as feature vectors
Each phrase is represented as a vector of its context. The frequency count of words appearing within a fixed sized window is aggregated and converted into pointwise mutual information(PMI) values
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.
