Paper: Language Independent NER Using A Maximum Entropy Tagger

ACL ID W03-0424
Title Language Independent NER Using A Maximum Entropy Tagger
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

Named Entity Recognition (NER) systems need to integrate a wide variety of information for optimal performance. This paper demonstrates that a maximum entropy tagger can effectively encode such information and identify named entities with very high accuracy. The tagger uses features which can be obtained for a vari- ety of languages and works effectively not only for English, but also for other languages such as German and Dutch.