Dependency parsing by inference over high-recall dependency predictions

Authors:
Sander Canisius;Toine Bogers;Antal van den Bosch;Jeroen Geertzen;Erik Tjong Kim Sang
Affiliations:
ILK/Computational Linguistics and AI Tilburg University, Tilburg, The Netherlands;ILK/Computational Linguistics and AI Tilburg University, Tilburg, The Netherlands;ILK/Computational Linguistics and AI Tilburg University, Tilburg, The Netherlands;ILK/Computational Linguistics and AI Tilburg University, Tilburg, The Netherlands;University of Amsterdam, Kruislaan, Amsterdam, The Netherlands
Venue:
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Year:
2006

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Abstract

As more and more syntactically-annotated corpora become available for a wide variety of languages, machine learning approaches to parsing gain interest as a means of developing parsers without having to repeat some of the labor-intensive and language-specific activities required for traditional parser development, such as manual grammar engineering, for each new language. The CoNLL-X shared task on multi-lingual dependency parsing (Buchholz et al., 2006) aims to evaluate and advance the state-of-the-art in machine learning-based dependency parsing by providing a standard benchmark set comprising thirteen languages. In this paper, we describe two different machine learning approaches to the CoNLL-X shared task.