Paper: Mistake-Driven Mixture Of Hierarchical Tag Context Trees

ACL ID P97-1030
Title Mistake-Driven Mixture Of Hierarchical Tag Context Trees
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997

This paper proposes a mistake-driven mix- ture method for learning a tag model. The method iteratively performs two proce- dures: 1. constructing a tag model based on the current data distribution and 2. updating the distribution by focusing on data that are not well predicted by the constructed model. The final tag model is constructed by mixing all the models according to their performance. To well reflect the data distribution, we repre- sent each tag model as a hierarchical tag (i.e. ,NTT 1 < proper noun < noun) con- text tree. By using the hierarchical tag context tree, the constituents of sequential tag models gradually change from broad coverage tags (e.g. ,noun) to specific excep- tional words that cannot be captured by generM tags. In other words, the method incorporates not only ...