An Empirical Evaluation on Statistical Parsing of Japanese Sentences Using Lexical Association Statistics

要旨

We are proposing a new framework of statistical language modeling which integrates lexical association statistics with syntactic preference, while maintaining the modularity of those different statistics types, facilitating both training of the model and analysis of its behavior. In this paper, we report the result of an empirical evaluation of our model, where the model is applied to disambiguation of dependency structures of Japanese sentences. We also discussed the room remained for further improvement based on our error analysis.

主題