An automatic classification of book texts to user-defined tags

Abstract

We describe work on automatically assigning labels to books using user-defined tags as the label set. Using supervised learning and exploring both binary and mul-ticlass classification, we train and test classifiers on several sets of features, focusing on the size of the sets, part-of-speech classes and named entities. Results indicate that a binary classifier, trained and tested on a feature space that consists of a limited selection of parts of speech as well as all frequent named entities, achieves a classification precision of 81%, significantly outperforming a baseline which assigns the top-10 most popular tags to each book. Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.