Paper: Conditional Structure Versus Conditional Estimation In NLP Models

ACL ID W02-1002
Title Conditional Structure Versus Conditional Estimation In NLP Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002
Authors

This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for maximum-entropy tagging, which tend to lower accuracy. Error analysis on part-of-speech tagging shows that the actual tagging errors made by the conditionally structured model derive not only from label bias, but also from other ways in which the in- dependence assumptions of the conditional model structure are unsuited to linguistic sequences. The paper presents new word-sense disambiguation and POS tagging experiments, and integrates apparently conflicting reports from other recent work.