Topic-oriented Words as Features for Named Entity Recognition

Abstract

Research has shown that topic-oriented words are often related to named entities and can be used for Named Entity Recognition. Many have proposed to measure topicality of words in terms of 'informativeness' based on global distributional characteristics of words in a corpus. However, this study shows that there can be large discrepancy between informativeness and topicality; empirically, informativeness based features can damage learning accuracy of NER. This paper proposes to measure words' topicality based on local distributional features specific to individual documents, and proposes methods to transform topicality into gazetteer-like features for NER by binning. Evaluated using five data..