A computational theory of grounding in natural language conversation
A computational theory of grounding in natural language conversation
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach
Computational Linguistics
Computer Supported Cooperative Work
Quantifying ellipsis in dialogue: an index of mutual understanding
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
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This paper presents a machine learning approach to bare sluice disambiguation in dialogue. We extract a set of heuristic principles from a corpus-based sample and formulate them as probabilistic Horn clauses. We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. Both learners perform well, yielding similar success rates of approx 90%. The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features.