Tagging and Chunking with Bigrams

In this paper we present an integrated system for tagging and chunking texts from a certain language. The approach is based on stochastic nite-state models that are learnt automatically. This includes bigram models or nite-state automata learnt using grammatical inference techniques. As the models involved in our system are learnt automatically, this is a very exible and portable system. In order to show the viability of our approach we present results for tagging and chunking using bigram models on the Wall Street Journal corpus. We have achieved an accuracy rate for tagging of 96.8%, and a precision rate for NP chunks of 94.6% with a recall rate of 93.6%.