Attention, intentions, and the structure of discourse
Computational Linguistics
Recognizing complex discourse acts: a tripartite plan-based model of dialogue
Recognizing complex discourse acts: a tripartite plan-based model of dialogue
Classifying cue phrases in text and speech using machine learning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
An Investigation of Transformation-Based Learning in Discourse
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
Topic identification in discourse
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Learning features that predict cue usage
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Can We Make Information Extraction More Adaptive?
Information Extraction: Towards Scalable, Adaptable Systems
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Randomized rule selection in transformation-based learning: a comparative study
Natural Language Engineering
A finite state and data-oriented method for grapheme to phoneme conversion
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Man vs. machine: a case study in base noun phrase learning
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Using Dialogue Features to Predict Trouble During Collaborative Learning
User Modeling and User-Adapted Interaction
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Computational Linguistics
Coaxing confidences from an old friend: probabilistic classifications from transformation rule lists
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Development of a machine learnable discourse tagging tool
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Training a Dialogue Act Tagger for human-human and human-computer travel dialogues
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
FLSA: extending latent semantic analysis with features for dialogue act classification
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dialogue act recognition under uncertainty using bayesian networks
Natural Language Engineering
Automatic annotation of context and speech acts for dialogue corpora
Natural Language Engineering
Natural Language Processing as a Foundation of the Semantic Web
Foundations and Trends in Web Science
Investigating the portability of corpus-derived cue phrases for dialogue act classification
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Dialogue segmentation with large numbers of volunteer internet annotators
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Tagging and linking web forum posts
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Classifying dialogue acts in one-on-one live chats
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Modeling socio-cultural phenomena in discourse
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Review: Some background on dialogue management and conversational speech for dialogue systems
Computer Speech and Language
RDRCE: combining machine learning and knowledge acquisition
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Error analysis of dialogue act classification
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Text based dialog act classification for multiparty meetings
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Transforming trees to improve syntactic convergence
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.00 |
For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task.