Paper: Tagging And Chunking With Bigrams

ACL ID C00-2089
Title Tagging And Chunking With Bigrams
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2000
Authors

In this paper we present an integrated system for tagging and chunking texts from a certain language. The approach is based on stochastic finite-state models that are learnt automatically. This includes bigrmn models or tinite-state automata learnt using grammatical inference techniques. As the models in- volved in our system are learnt automatically, this is a very flexible and portable system. Itl order to show the viability of our approach we t)resent results for tagging mid chunking using bi- grain models on the Wall Street Journal corpus.