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.
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Loni, B., van Tulder, G., Wiggers, P., Tax, D.M.J., Loog, M. (2011). Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_31
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DOI: https://doi.org/10.1007/978-3-642-23538-2_31
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