A Stochastic Parser Based on a Structural Word Prediction Model

n this paper, we present a stochastic language model using dependency. This model considers a sentence as a word sequence and predicts each word fi'om left to right. The history at each step of pre- diction is a sequence of partial parse tj'ees covering the preceding words. First our model predicts the partial parse trees which have a dependency relation with the next word among them 'and then predicts the next word from only the trees which have a dependency relation with the next word. Our model is a generarive stochastic model, thus this can be used not only as a parser but also as a language model of a speech recognizer. In our experiment, we prepared about 1,000 syntactically annotated Japanese sentences extracted fi'om a financial newspaper and estime;ted the parameters of our model. We built a parser based on our model and tested it on approximately 100 sentences of the same newspaper. The accm'acy of the dependency relation was 89.9%, the highest. accuracy level obtained by Japanese stocha.stic parsers