Enhanced Question Classification with Optimal Combination of Features

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. Different lexical, syntactical and semantic features can be extracted from a question. In this work we introduce two new semantic features which improve the accuracy of classification. Furthermore, we developed a weighed approach to optimally combine different features. We also applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. We adopted 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. We tested our proposed approaches on the well-known UIUC dataset and succeeded to achieve a new record on the accuracy of classification on this dataset.

Subject

Question Classification
Question Answering Systems
Lexical Features
Syntactical Features
Semantic Features
Combining Features
Support Vector Machines
Back-Propagation Neural Networks

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master thesis

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