In the present work we used a word clustering algorithm based on the perplexity criterion, in a Dialogue Act detection framework in order to model the structure of the speech of a user at a dialogue system. Specifically, we constructed an n-gram based model for each target Dialogue Act, computed over the word classes. Then we evaluated the performance of our dialogue system on ten different types of dialogue acts, using an annotated database which contains 1,403,985 unique words. The results were very promising since we achieved about 70% of accuracy using trigram based models. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Mporas, I., Lyras, D. P., Sgarbas, K. N., & Fakotakis, N. (2007). Detection of dialogue acts using perplexity-based word clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4629 LNAI, pp. 638–643). Springer Verlag. https://doi.org/10.1007/978-3-540-74628-7_82
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