Abstract
This paper extends a novel technique for the classification of short texts as Dialogue Acts, based on structural information contained in function words. It investigates the new challenge of discriminating between instructions and a non-instruction mix of questions and statements. The proposed technique extracts features by replacing function words with numeric tokens and replacing each content word with a standard numeric wildcard token. Consequently this is a potentially challenging task for the function-word based approach as the salient feature of an instruction is an imperative verb, which will always be replaced by a wildcard. Nevertheless, the results of the decision tree classifiers produced provide evidence for potentially highly effective classification and they are comparable with initial work on question classification. Improved classification accuracy is expected in future through optimisation of feature extraction.
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O’Shea, J., Bandar, Z., Crockett, K. (2011). Using a Slim Function Word Classifier to Recognise Instruction Dialogue Acts. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_4
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DOI: https://doi.org/10.1007/978-3-642-22000-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21999-3
Online ISBN: 978-3-642-22000-5
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