In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. We show how a datadriven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transformation techniques to produce non-projective structures. Experiments using data from the Prague Dependency Treebank show that the combined system can handle nonprojective constructions with a precision sufficient to yield a significant improvement in overall parsing accuracy. This leads to the best reported performance for robust non-projective parsing of Czech. 1

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Despite a long and venerable tradition in descriptive linguistics, dependency grammar has until recently played a fairly marginal role both in theoretical linguistics and in natural language processing. The increasing interest in dependency-based

We present a parser for German that achieves a competitive accuracy on unrestricted input while maintaining a coverage of 100%. By writing well-formedness rules as declarative, defeasible constraints that integrate di#erent sources of linguistic knowledge, very high robustness is achieved against all sorts of extragrammatical constructions.

We investigate the utility of supertag information for guiding an existing dependency parser of German. Using weighted constraints to integrate the additionally available information, the decision process of the parser is influenced by changing its preferences, without excluding alternative structural interpretations from being considered. The paper reports on a series of experiments using varying models of supertags that significantly increase the parsing accuracy. In addition, an upper bound on the accuracy that can be achieved with perfect supertags is estimated. 1

Freer-word-order languages such as German exhibit linguistic phenomena that present unique challenges to traditional CFG parsing. Such phenomena produce discontinuous constituents, which are not naturally modelled by projective phrase structure trees. In this paper, we examine topological field parsing, a shallow form of parsing which identifies the major sections of a sentence in relation to the clausal main verb and the subordinating heads. We report the results of topological field parsing of German using the unlexicalized, latent variable-based Berkeley parser (Petrov et al., 2006) Without any language- or model-dependent adaptation, we achieve state-of-the-art results on the TüBa-D/Z corpus, and a modified NE-GRA corpus that has been automatically annotated with topological fields (Becker and Frank, 2002). We also perform a qualitative error analysis of the parser output, and discuss strategies to further improve the parsing results.

In recent years, research in parsing has extended in several new directions. One of

This paper reports on a hybrid architecture for computational anaphora resolution (CAR) of German that combines a rule-based pre-filtering component with a memory-based resolution module (using the Tilburg Memory Based Learner – TiMBL). The data source is provided by the TüBa-D/Z treebank of German newspaper text (Telljohann et al. 04) that is annotated with anaphoric relations. The CAR experiments performed on these treebank data corroborate the importance of modelling aspects of discourse structure for robust, data-driven anaphora resolution. The best result with an F-measure of 0.734 achieved by these experiments outperforms the results reported by (Schiehlen 04), the only other study of German CAR that is based on newspaper treebank data. 1

The manual design of grammars for accurate natural language analysis is an iterative process; while modelling decisions usually determine parser behaviour, evidence from analysing more or different input can suggest unforeseen regularities, which leads to a reformulation of rules, or even to a different model of previously analysed phenomena. We describe an implementation of Weighted Constraint Dependency Grammar that supports the grammar writer by providing display, automatic analysis, and diagnosis of dependency analyses and allows the direct exploration of alternative analyses and their status under the current grammar.

Syntactic analysis is useful for many natural language processing applications requiring further semantic analysis. Recent research in statistical parsing has produced a number of highperformance parsers using probabilistic context-free (PCFG) models to parse English text, such as (Collins, 2003; Charniak and Johnson, 2005). Problems arise, however, when applying these methods to parse sentences in freer-word-order languages. Such languages as Russian, Warlpiri, and German feature syntactic constructions that produce discontinuous constituents, directly violating one of the crucial assumptions of context-free models of syntax. While PCFG technologies may thus be inadequate for full syntactic analysis of all phrasal structure in these languages, clausal structure can still be fruitfully parsed with these methods. In particular, we examine applying PCFG parsing to parse the topological field structure of German. These topological fields provide a high-level description of the major sections of a clause in relation to the clausal main verb and the subordinating heads and appear in strict linear sequences amenable to PCFG parsing. They are useful for tasks such as deep syntactic analysis, part-of-speech tagging and coreference resolution. In this work, we apply an unlexicalized, latent variable-based parser (Petrov et al., 2006) to