Ranking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron

@inproceedings{Collins2002RankingAF,
  title={Ranking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron},
  author={Michael Collins},
  booktitle={ACL},
  year={2002}
}
This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the application being the recovery of named-entity boundaries in a corpus of web data. The first approach uses a boosting algorithm for ranking problems. The second approach uses the voted perceptron algorithm. Both algorithms give comparable, significant improvements over the maximum-entropy baseline. The voted perceptron algorithm can be considerably more efficient to train, at some cost in… CONTINUE READING
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