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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Employing Oracle Confusion for Parse Quality Estimation
verfasst von : Sambhav Jain, Naman Jain, Bhasha Agrawal, Rajeev Sangal
Erschienen in: Computational Linguistics and Intelligent Text Processing
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We propose an approach for
Parse Quality Estimation
based on the dynamic computation of an entropy-based confusion score for directed arcs and for joint prediction of directed arcs and their dependency labels, in a typed dependency parsing framework. This score accompanies a parsed output and aims to present an exhaustive picture of the
parse quality
, detailed down to each arc of the parse tree. The methodology explores the confusion encountered by the oracle of a transition-based data-driven dependency parser. We support our hypothesis by analytically illustrating, for 18 languages, that the arcs with high confusion scores are notably the predominant parsing errors.