From symbolic to sub-symbolic information in question classification

This paper presents and evaluates a rule-based question classifier that partially founds its performance in the detection of the question headword and in its mapping into the target category through the use of WordNet, and uses the rule-base classifier as a features’ provider of a machine learning-basedquestion classifier.

Abstract

Question Answering (QA) is undoubtedly a growing field of current research in Artificial Intelligence. Question classification, a QA subtask, aims to associate a category to each question, typically representing the semantic class of its answer. This step is of major importance in the QA process, since it is the basis of several key decisions. For instance, classification helps reducing the number of possible answer candidates, as only answers matching the question category should be taken into account. This paper presents and evaluates a rule-based question classifier that partially founds its performance in the detection of the question headword and in its mapping into the target category through the use of WordNet. Moreover, we use the rule-based classifier as a features' provider of a machine learning-based question classifier. A detailed analysis of the rule-base contribution is presented. Despite using a very compact feature space, state of the art results are obtained.