Hard Constraints for Grammatical Function Labelling

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1087–1097, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Hard Constraints for Grammatical Function Labelling Wolfgang Seeker University of Stuttgart Institut f¨ur Maschinelle Sprachverarbeitung seeker@ims.uni-stuttgart.de Ines Rehbein University of Saarland Dep. for Comp. Linguistics & Phonetics rehbein@coli.uni-sb.de Jonas Kuhn University of Stuttgart Institut f¨ur Maschinelle Sprachverarbeitung jonas@ims.uni-stuttgart.de Josef van Genabith Dublin City University CNGL and School of Computing josef@computing.dcu.ie Abstract For languages with (semi-) free word or- der (such as German), labelling gramma- tical 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 par- ticular for argument grammatical func- tions, in an intrinsic evaluation, and, im- portantly, grammar coverage for treebank- based (Lexical-Functional) grammar ac- quisition and parsing, in an extrinsic eval- uation. 1 Introduction Phrase or constituent structure is often regarded as an analysis step guiding semantic interpretation, while grammatical functions (i. e. subject, object, modifier etc.) provide important information rele- vant to determining predicate-argument structure. In languages with restricted word order (e. g. English), core grammatical functions can often be recovered from configurational information in constituent structure analyses. By contrast, sim- ple constituent structures are not sufficient for less configurational languages, which tend to encode grammatical functions by morphological means (Bresnan, 2001). Case features, for instance, can be important indicators of grammatical functions. Unfortunately, many of these languages (including German) exhibit strong syncretism where morpho- logical cues can be highly ambiguous with respect to functional information. Statistical classifiers have been successfully used to label constituent structure parser output with grammatical function information (Blaheta and Charniak, 2000; Chrupała and Van Genabith, 2006). However, as these approaches tend to use only limited and local context information for learning and prediction, they often fail to en- force simple yet important global linguistic con- straints that exist for most languages, e. g. that there will be at most one subject (object) per sen- tence/clause.1 “Hard” linguistic constraints, such as these, tend to affect mostly the “core grammatical func- tions”, i. e. the argument functions (rather than e. g. adjuncts) of a particular predicate. As these functions constitute the core meaning of a sen- tence (as in: who did what to whom), it is impor- tant to get them right. We present a system that adds grammatical function labels to constituent parser output for German in a postprocessing step. We combine a statistical classifier with an inte- ger linear program (ILP) to model non-violable global linguistic constraints, restricting the solu- tion space of the classifier to those labellings that comply …

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