Genre identification for office document search and browsing

Abstract

When searching or browsing documents, the genre of a document is an important consideration that complements topical characterization. We examine design considerations for automatic tagging of office document pages with genre membership. These include selecting features that characterize genre-related information in office documents, examining the utility of text-based features and image-based features, and proposing a simple ensemble method to improve the performance of genre identification. Experiments were conducted on the open-set identification of four coarse office document genres: technical paper, photo, slide, and table. Our experiments show that when combined with image-based features, text-based features do not significantly influence performance. These results provide support for a topic-independent approach to identification of coarse office document genres. Experiments also show that our simple ensemble method significantly improves performance relative to using a support vector machine (SVM) classifier alone. We demonstrate the utility of our approach by integrating our automatic genre tags in a faceted search and browsing application for office document collections.