Vine parsing and minimum risk reranking for speed and precision

Authors:
Markus Dreyer;David A. Smith;Noah A. Smith
Affiliations:
Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD
Venue:
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Year:
2006

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Abstract

We describe our entry in the CoNLL-X shared task. The system consists of three phases: a probabilistic vine parser (Eisner and N. Smith, 2005) that produces unlabeled dependency trees, a probabilistic relation-labeling model, and a discriminative minimum risk reranker (D. Smith and Eisner, 2006). The system is designed for fast training and decoding and for high precision. We describe sources of cross-lingual error and ways to ameliorate them. We then provide a detailed error analysis of parses produced for sentences in German (much training data) and Arabic (little training data).