Paper: Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging

ACL ID C08-1098
Title Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

We present a HMM part-of-speech tag- ging method which is particularly suited for POS tagsets with a large number of fine-grainedtags. Itisbasedonthreeideas: (1) splitting of the POS tags into attribute vectors and decomposition of the contex- tual POS probabilities of the HMM into a product of attribute probabilities, (2) esti- mation of the contextual probabilities with decision trees, and (3) use of high-order HMMs. In experiments on German and Czech data, our tagger outperformed state- of-the-art POS taggers.