Paper: HMM Specialization With Selective Lexicalization

ACL ID W99-0615
Title HMM Specialization With Selective Lexicalization
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 1999
Authors

We present a technique which complements Hidden Markov Models by incorporating some lexicalized states representing syntactically un- common words. 'Our approach examines the distribution of transitions, selects the uncom- mon words, and makes lexicalized states for the words. We perfor'med a part-of-speech tagging experiment on the Brown corpus to evaluate the resultant language model and discovered that this technique improved the tagging accuracy by 0.21% at the 95% level of confidence.