Paper: Part-Of-Speech Tagging Using A Variable Memory Markov Model

ACL ID P94-1025
Title Part-Of-Speech Tagging Using A Variable Memory Markov Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1994
Authors

We present a new approach to disambiguating syn- tactically ambiguous words in context, based on Variable Memory Markov (VMM) models. In con- trast to fixed-length Markov models, which predict based on fixed-length histories, variable memory Markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. In a test of a VMM based tagger on the Brown corpus, 95.81% of tokens are correctly classified.