C4.5: programs for machine learning
C4.5: programs for machine learning
Verb phrase ellipsis: form, meaning, and processing
Verb phrase ellipsis: form, meaning, and processing
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
An empirical approach to VP ellipsis
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A discourse copying algorithm for ellipsis and anaphora resolution
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
A simple pattern-matching algorithm for recovering empty nodes and their antecedents
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Semantic role labeling of implicit arguments for nominal predicates
Computational Linguistics
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This paper describes a Verb Phrase Ellipsis (VPE) detection system, built for robustness, accuracy and domain independence. The system is corpus-based, and uses a variety of machine learning techniques on free text that has been automatically parsed using two different parsers. Tested on a mixed corpus comprising a range of genres, the system achieves a 72% F1-score. It is designed as the first stage of a complete VPE resolution system that is input free text, detects VPEs, and proceeds to find the antecedents and resolve them.