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Author Fernández, Raquel ♦ Ginzburg, Jonathan ♦ Lappin, Shalom
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Significant Predictive Power ♦ Domain Independent Feature ♦ Different Machine ♦ Rule-based Learning Algorithm ♦ Similar Success Rate ♦ Horn Clause ♦ Corpus-based Sample ♦ Machine Learning Approach ♦ Sluice Disambiguation ♦ Input Dataset ♦ Heuristic Principle ♦ Probabilistic Horn Clause ♦ Memory-based System
Description 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. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2004-01-01
Publisher Institution Proceedings of the 20 th international conference on computational linguistics