Experiments with Sentence Classification

We present a set of experiments involving sentence classification, addressing issues of representation and feature selection, and we compare our findings with similar results from work on the more general text classification task. The domain of our investigation is an email-based help-desk corpus. Our investigations compare the use of various popular classification algorithms with various popular feature selection methods. The results highlight similarities between sentence and text classification, such as the superiority of Support Vector Machines, as well as differences, such as a lesser extent of the usefulness of features selection on sentence classification, and a detrimental effect of common preprocessing techniques (stop-word removal and lemmatization).