Paper: A Maximum Entropy Model For Part-Of-Speech Tagging

ACL ID W96-0213
Title A Maximum Entropy Model For Part-Of-Speech Tagging
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 1996

This paper presents a statistical model which trains from a corpus annotated with Part-Of- Speech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" to predict the POS tag. Furthermore, this paper demonstrates the use of specialized fea- tures to model difficult tagging decisions, discusses the corpus consistency problems discovered during the implementation of these features, and proposes a training strategy that mitigates these problems.