Question classification by weighted combination of lexical, syntactic and semantic features

Authors:
Babak Loni;Gijs Van Tulder;Pascal Wiggers;David M. J. Tax;Marco Loog
Affiliations:
Delft University of Technology, Pattern Recognition Laboratory, Delft, The Netherlands;Delft University of Technology, Pattern Recognition Laboratory, Delft, The Netherlands;Delft University of Technology, Pattern Recognition Laboratory, Delft, The Netherlands;Delft University of Technology, Pattern Recognition Laboratory, Delft, The Netherlands;Delft University of Technology, Pattern Recognition Laboratory, Delft, The Netherlands
Venue:
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Year:
2011

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Abstract

We developed a learning-based question classifier for question answering systems. A question classifier tries to predict the entity type of the possible answers to a given question written in natural language. We extracted several lexical, syntactic and semantic features and examined their usefulness for question classification. Furthermore we developed a weighting approach to combine features based on their importance. Our result on the well-known TREC questions dataset is competitive with the state-of-the-art on this task.