Paper: Maximum Entropy Models For Named Entity Recognition

ACL ID W03-0420
Title Maximum Entropy Models For Named Entity Recognition
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional non- annotated data. In turn, these lists are incor- porated into the final recognizer to further im- prove the recognition accuracy.