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. 1

Citations

464	Inducing Features of Random Fields - Pietra, Pietra, et al. - 1997
355	Generalized Iterative Scaling for Log-Linear Models - Darroch, Ratcliff - 1972
238	Nymble: a High-Performance Learning Name-finder - Bikel, Miller, et al. - 1997
115	A Maximum Entropy Approach to Named Entity Recognition - Borthwick - 1999
58	Description of the SRA System as Used for MUC-6 - Krupka - 1995
38	Description of the LTG system used for MUC-7 - Mikheev, Grover, et al. - 1998
27	An algorithm that learns what’s in a name - Weischedel - 1999
27	Muc-7 named entity task definition (version - Chinchor - 1998
1	Named entity recognition without gazetteers - Miller, Crystal, et al. - 1999