Large Scale Unstructured Document Classification Using Unlabeled Data and Syntactic Information

Most document classification systems consider only the distribution of content words of the documents, ignoring the syntactic information underlying the documents though it is also an important factor. In this paper, we present an approach for classifying large scale unstructured documents by incorporating both lexical and syntactic information of documents. For this purpose, we use the co-training algorithm, a partially supervised learning algorithm, in which two separated views for the training data are employed and the small number of labeled data are augmented by a large number of unlabeled data. Since both lexical and syntactic information can play roles of separated views for the unstructured documents, the co-training algorithm enhances the performance of document classification using both of them and a large number of unlabeled documents. The experimental results on Reuters-21578 corpus and TREC-7 filtering documents show the effectiveness of unlabeled documents and the use of both lexical and syntactic information.