Question classification with support vector machines and error correcting codes

In this paper we consider a machine learning technique for question classification. The goal is to replace our regular expression based classifier with a classifier that learns from a set of labeled questions. We have realized that an enourmous amount of time is required to create a rich collection of patterns and keywords for a good coverage of questions in an open-domain application. We decided to use support vector machines, since they have been successfully used for a number of benchmark problems. Although the support vector machines are inherently binary classifiers, it is possible to extend their use as multi-class classifiers using binary codes. We represent questions as frequency weighted vectors of salient terms. We compare our approcah to related work that uses relatively complex syntactic/semantic processing to create features and a sparse network of linear units to classify questions. We provide results to show performance of the method.