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2007

Online Learning for Deterministic Dependency Parsing

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Online Learning for Deterministic Dependency Parsing
Deterministic parsing has emerged as an effective alternative for complex parsing algorithms which search the entire search space to get the best probable parse tree. In this paper, we present an online large margin based training framework for deterministic parsing using Nivre’s Shift-Reduce parsing algorithm. Online training facilitates the use of high dimensional features without creating memory bottlenecks unlike the popular SVMs. We participated in the CoNLL Shared Task-2007 and evaluated our system for ten languages. We got an average multilingual labeled attachment score of 74.54 % (with 65.50% being the average and 80.32% the highest) and an average multilingual unlabeled attachment score of 80.30% (with
Prashanth Mannem
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Prashanth Mannem
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