Paper: Classifying Ellipsis In Dialogue: A Machine Learning Approach

ACL ID C04-1035
Title Classifying Ellipsis In Dialogue: A Machine Learning Approach
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper presents a machine learning approach to bare sluice disambiguation in dialogue. We ex- tract 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 an- notate an input dataset, and run two di erent ma- chine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. Both learners perform well, yielding simi- lar success rates of approx 90%. The results show that the features in terms of which we formulate our heuristic principles have signi cant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features.