Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Transcriber: Development and use of a tool for assisting speech corpora production
Speech Communication - Special issue on speech annotation and corpus tools
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
An empirical investigation of proposals in collaborative dialogues
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Domain adaptation with unlabeled data for dialog act tagging
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
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This paper reports studies on annotating and automatically detecting dialog acts in human-human spoken dialogs. The work reposes on three hypotheses: first, the succession of dialog acts is strongly constrained; second, the initial word and semantic class of word are more important for identifying dialog acts than the complete exact word sequence of an utterance; third, most of the important information is encoded in specific entities. A memory based learning approach is used to detect dialog acts. For each utterance unit, eight dialog acts are systematically annotated. Experiments have been conducted using different levels of information, with and without the use of dialog history information. In order to assess the generality of the method, the specific entity tag based model trained on a French corpus was tested on an English corpus for a similar task and on a French corpus from a different domain. A correct dialog act detection rate of about 86% is obtained for the same domain/language condition and 77% for the cross-language or cross-domain conditions.