A pipeline model for bottom-up dependency parsing

Authors:
Ming-Wei Chang;Quang Do;Dan Roth
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Year:
2006

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Abstract

We present a new machine learning framework for multi-lingual dependency parsing. The framework uses a linear, pipeline based, bottom-up parsing algorithm, with a look ahead local search that serves to make the local predictions more robust. As shown, the performance of the first generation of this algorithm is promising.