Paper: Beyond N In N-Gram Tagging

ACL ID P04-2011
Title Beyond N In N-Gram Tagging
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

The Hidden Markov Model (HMM) for part-of-speech (POS) tagging is typi- cally based on tag trigrams. As such it models local context but not global context, leaving long-distance syntactic relations unrepresented. Using n-gram models for n > 3 in order to incorporate global context is problematic as the tag sequences corresponding to higher order models will become increasingly rare in training data, leading to incorrect esti- mations of their probabilities. The trigram HMM can be extended with global contextual information, without making the model infeasible, by incor- porating the context separately from the POS tags. The new information incor- porated in the model is acquired through the use of a wide-coverage parser. The model is trained and tested on Dutch text from two different sou...