Latent semantic analysis for question classification with neural networks

An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Most approaches use features based on word unigrams which leads to large feature space. In this work we applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. We used two different classifiers: Back-Propagation Neural Networks (BPNN) and Support Vector Machines (SVM). We found that applying LSA on question classification can not only make the question classification more time efficient, but it also improves the classification accuracy by removing the redundant features. Furthermore, we discovered that when the original feature space is compact and efficient, its reduced space performs better than a large feature space with a rich set of features. In addition, we found that in the reduced feature space, BPNN performs better than SVMs which are widely used in question classification. Our result on the well known UIUC dataset is competitive with the state-of-the-art in this field, even though we used much smaller feature spaces.