Paper: Joint And Conditional Estimation Of Tagging And Parsing Models

ACL ID P01-1042
Title Joint And Conditional Estimation Of Tagging And Parsing Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001
Authors

This paper compares two different ways of estimating statistical language mod- els. Many statistical NLP tagging and parsing models are estimated by max- imizing the (joint) likelihood of the fully-observed training data. How- ever, since these applications only re- quire the conditional probability distri- butions, these distributions can in prin- ciple be learnt by maximizing the con- ditional likelihood of the training data. Perhaps somewhat surprisingly, models estimated by maximizing the joint were superior to models estimated by max- imizing the conditional, even though some of the latter models intuitively had access to “more information”.