Paper: Part-Of-Speech Tagging Using Virtual Evidence And Negative Training

ACL ID H05-1058
Title Part-Of-Speech Tagging Using Virtual Evidence And Negative Training
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

We present a part-of-speech tagger which introduces two new concepts: virtual evi- dence in the form of an observed child node, and negative training data to learn the conditional probabilities for the ob- served child. Associated with each word is a exible feature-set which can in- clude binary ags, neighboring words, etc. The conditional probability of Tag given Word + Features is implemented using a factored language-model with back-off to avoid data sparsity problems. This model remains within the framework of Dynamic Bayesian Networks (DBNs) and is conditionally-structured, but resolves the label bias problem inherent in the con- ditional Markov model (CMM).