An Integrated Statistical Model for Tagging and Chunking Unrestricted Text

In this paper, we present a corpus-based approach for tagging and chunking. The formalism used is based on stochastic finite-state automata. Therefore, it can include n-grams models or any stochastic finite-state automata learnt using grammatical inference techniques. As the models involved in our system are learnt automatically, it allows for a very flexible and portable system for different languages and chunk definitions. In order to show the viability of our approach, we present results for tagging and chunking using different combinations of bigrams and other more complex automata learnt by means of the Error Correcting Grammatical Inference (ECGI) algorithm. The experimentation was carried out on the Wall Street Journal corpus for English and on the Lexesp corpus for Spanish.

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