Pro3Gres Parser in the CoNLL Domain Adaptation Shared Task

Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL 2007, pp. 1161–1165, Prague, June 2007. c⃝2007 Association for Computational Linguistics Pro3Gres Parser in the CoNLL Domain Adaptation Shared Task Gerold Schneider and Kaarel Kaljurand and Fabio Rinaldi and Tobias Kuhn Institute of Computational Linguistics, University of Zurich Binzm¨uhlestrasse 14 CH - 8050 Zurich, Switzerland {gschneid,kalju,rinaldi,tkuhn}@ifi.uzh.ch Abstract We present Pro3Gres, a deep-syntactic, fast dependency parser that combines a hand- written competence grammar with proba- bilistic performance disambiguation and that has been used in the biomedical domain. We discuss its performance in the domain adap- tation open submission. We achieve aver- age results, which is partly due to difficulties in mapping to the dependency representation used for the shared task. 1 Introduction The Pro3Gres parser is a dependency parser that combines a hand-written grammar with probabilis- tic disambiguation. It is described in detail in (Schneider, 2007). It uses tagger and chunker pre-processors – parsing proper happens only be- tween heads of chunks – and a post-processor graph converter to capture long-distance dependencies. Pro3Gres is embedded in a flexible XML pipeline. It has been applied to many tasks, such as parsing biomedical literature (Rinaldi et al., 2006; Rinaldi et al., 2007) and the whole British National Cor- pus, and has been evaluated in several ways. We have achieved average results in the CoNLL do- main adaptation track open submission (Marcus et al., 1993; Johansson and Nugues, 2007; Kulick et al., 2004; MacWhinney, 2000; Brown, 1973). The performance of the parser is seriously affected by mapping problems to the particular dependency rep- resentation used in the shared task. The paper is structured as follows. We give a brief overview of the parser and its design policy in sec- tion 2, we describe the domain adaptations that we have used in section 3, comment on the results ob- tained in section 4 and conclude in section 5. 2 Pro3Gres and its Design Policy There has been growing interest in exploring the space between Treebank-trained probabilistic gram- mars (e.g. (Collins, 1999; Nivre, 2006)) and formal grammar-based parsers integrating statistics (e.g. (Miyao et al., 2005; Riezler et al., 2002)). We have developed a parsing system that explores this space, in the vein of systems like (Kaplan et al., 2004), using a linguistic competence grammar and a probabilistic performance disambiguation allow- ing us to explore interactions between lexicon and grammar (Sinclair, 1996). The parser has been ex- plicitly designed to be deep-syntactic like a formal grammar-based parser, by using a dependency rep- resentation that is close to LFG f-structure, but at the same time mostly context-free and integrating shallow approaches and aggressive pruning in or- der to keep search-spaces small, without permitting compromise on performance or linguistic adequacy. (Abney, 1995) establishes the chunks and dependen- cies model as a well-motivated linguistic theory. The non-local linguistic constraints that a hand-written grammar allows us to formulate, e.g. expressing X-bar principles or barring very marked construc- tions, further reduce parsing time by at least an order of magnitude. Since the grammar …

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