Paper: Named Entity Recognition: A Maximum Entropy Approach Using Global Information

ACL ID C02-1025
Title Named Entity Recognition: A Maximum Entropy Approach Using Global Information
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2002
Authors

This paper presents a maximum entropy-based named entity recognizer (NER). It differs from pre- vious 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 sentence- based classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves perfor- mance that is comparable to the best previous ma- chine learning-based NERs on MUC-6 and MUC-7 test data.