An Automatic Classification of Book Texts to User-Defined Tags

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 sev- eral sets of features, focusing on the size of the sets, part-of-speech classes and named entities. Results indi- cate that a binary classifier, trained and tested on a fea- ture 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 outper- forming a baseline which assigns the top-10 most pop- ular tags to each book.