Maximum entropy models for named entity recognition

Oliver Bender, Franz Josef Och, Hermann Ney
2003 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -   unpublished
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 nonannotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.
doi:10.3115/1119176.1119196 fatcat:q4a4zl7tlret3ajs4et3su2idm