PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hard Constraints for Grammatical Function Labelling
Wolfgang Seeker, Ines Rehbein, Jonas Kuhn and Josef van Genabith
In: The 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), 11-16 Jul 2010, Uppsala, Finland.


For languages with (semi-) free word order (such as German), labelling grammatical functions on top of phrase-structural constituent analyses is crucial for making them interpretable. Unfortunately, most statistical classifiers consider only local information for function labelling and fail to capture important restrictions on the distribution of core argument functions such as subject, object etc., namely that there is at most one subject (etc.) per clause. We augment a statistical classifier with an integer linear program imposing hard linguistic constraints on the solution space output by the classifier, capturing global distributional restrictions. We show that this improves labelling quality, in particular for argument grammatical functions, in an intrinsic evaluation, and, importantly, grammar coverage for treebankbased (Lexical-Functional) grammar acquisition and parsing, in an extrinsic evaluation.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7986
Deposited By:Ines Rehbein
Deposited On:17 March 2011