Extracting characteristic words of text using neural networks

In this paper, we consider models for estimating categories of documents and extracting characteristic words of such categories. To this end, we focus on three models, i.e., naive Bayes and two types of neural networks formalized as statistical models. Here, suitable categories of documents are estimated based on posterior probabilities, and characteristic words are extracted based on the magnitude of resulting parameter values. In our experiments using a set of real Web pages, we compare these models in the aspect of categorization performances and extraction capabilities of characteristic words.