Paper: A Flexible POS Tagger Using An Automatically Acquired Language Model

ACL ID P97-1031
Title A Flexible POS Tagger Using An Automatically Acquired Language Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997
Authors

We present an algorithm that automati- cally learns context constraints using sta- tistical decision trees. We then use the ac- quired constraints in a flexible POS tag- ger. The tagger is able to use informa- tion of any degree: n-grams, automati- cally learned context constraints, linguis- tically motivated manually written con- straints, etc. The sources and kinds of con- straints are unrestricted, and the language model can be easily extended, improving the results. The tagger has been tested and evaluated on the WSJ corpus.