Paper: POS Tags And Decision Trees For Language Modeling

ACL ID W99-0617
Title POS Tags And Decision Trees For Language Modeling
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 1999

Language models for speech recognition con- centrate solely on recognizing the words that were spoken. In this paper, we advocate re- defining the speech recognition problem so that its goal is to find both the best sequence of words and their POS tags, and thus incorpo- rate POS tagging. To use POS tags effectively, we use clustering and decision tree algorithms, which allow generalizations between POS tags and words to be effectively used in estimating the probability distributions. We show that our POS model gives, a reduction in word error rate and perplexity for the Trains corpus in compar- ison to word and class-based approaches. By using the Wall Street Journal corpus, we show that this approach scales up when more training data is available.