Paper: Error-Driven HMM-Based Chunk Tagger With Context-Dependent Lexicon

ACL ID W00-1309
Title Error-Driven HMM-Based Chunk Tagger With Context-Dependent Lexicon
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 2000

This paper proposes a new error-driven HMM- based text chunk tagger with context-dependent lexicon. Compared with standard HMM-based tagger, this tagger uses a new Hidden Markov Modelling approach which incorporates more contextual information into a lexical entry. Moreover, an error-driven learning approach is adopted to decrease the memory requirement by keeping only positive lexical entries and makes it possible to further incorporate more context- dependent lexical entries. Experiments show that this technique achieves overall precision and recall rates of 93.40% and 93.95% for all chunk types, 93.60% and 94.64% for noun phrases, and 94.64% and 94.75% for verb phrases when trained on PENN WSJ TreeBank section 00-19 and tested on section 20-24, while 25-fold validation experiments of PE...