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Named Entity Recognition: A Maximum Entropy Approach Using Global Information

by: Haoi Leong Chieu, and Hwee Tou Ng

In: Proceedings COLING 2002 (2002) .

Abstract

This paper presents a maximum entropy-based named entity recognizer NER. It differs from previous machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentencebased classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves performance that is comparable to the best previous machine learning-based NERs on MUC-6 and MUC-7 test data.

BibTeX record

@inproceedings{Chieu:2002,
  abstract = {This paper presents a maximum entropy-based named entity recognizer (NER). It differs from previous machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentencebased classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves performance that is comparable to the best previous machine learning-based NERs on MUC-6 and MUC-7 test data.},
  added-at = {2007-12-14T02:37:20.000+0100},
  author = {Chieu, Haoi Leong and Ng, Hwee Tou},
  biburl = {http://www.bibsonomy.org/bibtex/2d4f233fd44172dfcf0d98dd03bc781e1/diego_ma},
  booktitle = {Proceedings COLING 2002},
  interhash = {3cf03ab32dea86078e392abb70b62066},
  intrahash = {d4f233fd44172dfcf0d98dd03bc781e1},
  keywords = {named_entities machine_learning},
  timestamp = {2007-12-14T02:37:20.000+0100},
  title = {Named Entity Recognition: A Maximum Entropy Approach Using Global Information},
  year = 2002
}

Endnote record