Cross-Domain Dependency Parsing Using a Deep Linguistic Grammar

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 378–386, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Cross-Domain Dependency Parsing Using a Deep Linguistic Grammar Yi Zhang LT-Lab, DFKI GmbH and Dept of Computational Linguistics Saarland University D-66123 Saarbr¨ucken, Germany yzhang@coli.uni-sb.de Rui Wang Dept of Computational Linguistics Saarland University 66123 Saarbr¨ucken, Germany rwang@coli.uni-sb.de Abstract Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain. In this paper, we propose two different approaches to pro- duce syntactic dependency structures us- ing a large-scale hand-crafted HPSG gram- mar. The dependency backbone of an HPSG analysis is used to provide general linguistic insights which, when combined with state-of-the-art statistical dependency parsing models, achieves performance im- provements on out-domain tests.† 1 Introduction Syntactic dependency parsing is attracting more and more research focus in recent years, par- tially due to its theory-neutral representation, but also thanks to its wide deployment in various NLP tasks (machine translation, textual entailment recognition, question answering, information ex- traction, etc.). In combination with machine learn- ing methods, several statistical dependency pars- ing models have reached comparable high parsing accuracy (McDonald et al., 2005b; Nivre et al., 2007b). In the meantime, successful continuation of CoNLL Shared Tasks since 2006 (Buchholz and Marsi, 2006; Nivre et al., 2007a; Surdeanu et al., 2008) have witnessed how easy it has become to train a statistical syntactic dependency parser pro- vided that there is annotated treebank. While the dissemination continues towards var- ious languages, several issues arise with such purely data-driven approaches. One common observation is that statistical parser performance drops significantly when tested on a dataset differ- ent from the training set. For instance, when using †The first author thanks the German Excellence Cluster of Multimodal Computing and Interaction for the support of the work. The second author is funded by the PIRE PhD scholarship program. the Wall Street Journal (WSJ) sections of the Penn Treebank (Marcus et al., 1993) as training set, tests on BROWN Sections typically result in a 6-8% drop in labeled attachment scores, although the av- erage sentence length is much shorter in BROWN than that in WSJ. The common interpretation is that the test set is heterogeneous to the training set, hence in a different “domain” (in a loose sense). The typical cause of this is that the model overfits the training domain. The concerns over random choice of training corpus leading to linguistically inadequate parsing systems increase over time. While the statistical revolution in the field of computational linguistics gaining high pub- licity, the conventional symbolic grammar-based parsing approaches have undergone a quiet pe- riod of development during the past decade, and reemerged very recently with several large scale grammar-driven parsing systems, benefiting from the combination of well-established linguistic the- ories and data-driven stochastic models. The ob- vious advantage of such systems over pure statis- tical parsers is their usage of hand-coded linguis- tic knowledge irrespective of the training data. A common problem with grammar-based parser …

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