Making large-scale support vector machine learning practical
Advances in kernel methods
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Exploring evidence for shallow parsing
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Our research is motivated by the observation that NLP systems frequently mislabel passive voice verb phrases as being in the active voice when there is no auxiliary verb (e.g., "The man arrested had a long record"). These errors directly impact thematic role recognition and NLP applications that depend on it. We present a learned classifier that can accurately identify reduced passive voice constructions in shallow parsing environments.