Maximum entropy models for named entity recognition

Abstract

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 incorporated into the final recognizer to further improve the recognition accuracy.