The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Dialogue act recognition with Bayesian networks for Dutch dialogues
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
Domain kernels for text categorization
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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When engaged in dialogues, people perform communicative actions to pursue specific communicative goals. Speech acts recognition attracted computational linguistics since long time and could impact considerably a huge variety of application domains. We study the task of automatic labeling dialogues with the proper dialogue acts, relying on empirical methods and simply exploiting lexical semantics of the utterances. In particular, we present some experiments in supervised and unsupervised framework on both an English and an Italian corpus of dialogue transcriptions. The evaluation displays encouraging results in both languages, especially in the unsupervised version of the methodology.