Creating a CCGbank and a Wide-Coverage CCG Lexicon for German

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 505–512, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Creating a CCGbank and a wide-coverage CCG lexicon for German Julia Hockenmaier Institute for Research in Cognitive Science University of Pennsylvania Philadelphia, PA 19104, USA juliahr@cis.upenn.edu Abstract We present an algorithm which creates a German CCGbank by translating the syn- tax graphs in the German Tiger corpus into CCG derivation trees. The resulting cor- pus contains 46,628 derivations, covering 95% of all complete sentences in Tiger. Lexicons extracted from this corpus con- tain correct lexical entries for 94% of all known tokens in unseen text. 1 Introduction A number of wide-coverage TAG, CCG, LFG and HPSG grammars (Xia, 1999; Chen et al., 2005; Hockenmaier and Steedman, 2002a; O’Donovan et al., 2005; Miyao et al., 2004) have been ex- tracted from the Penn Treebank (Marcus et al., 1993), and have enabled the creation of wide- coverage parsers for English which recover local and non-local dependencies that approximate the underlying predicate-argument structure (Hocken- maier and Steedman, 2002b; Clark and Curran, 2004; Miyao and Tsujii, 2005; Shen and Joshi, 2005). However, many corpora (B¨ohomv´a et al., 2003; Skut et al., 1997; Brants et al., 2002) use dependency graphs or other representations, and the extraction algorithms that have been developed for Penn Treebank style corpora may not be im- mediately applicable to this representation. As a consequence, research on statistical parsing with “deep” grammars has largely been confined to En- glish. Free-word order languages typically pose greater challenges for syntactic theories (Rambow, 1994), and the richer inflectional morphology of these languages creates additional problems both for the coverage of lexicalized formalisms such as CCG or TAG, and for the usefulness of de- pendency counts extracted from the training data. On the other hand, formalisms such as CCG and TAG are particularly suited to capture the cross- ing dependencies that arise in languages such as Dutch or German, and by choosing an appropriate linguistic representation, some of these problems may be mitigated. Here, we present an algorithm which translates the German Tiger corpus (Brants et al., 2002) into CCG derivations. Similar algorithms have been developed by Hockenmaier and Steedman (2002a) to create CCGbank, a corpus of CCG derivations (Hockenmaier and Steedman, 2005) from the Penn Treebank, by C¸ akıcı (2005) to extract a CCG lex- icon from a Turkish dependency corpus, and by Moortgat and Moot (2002) to induce a type-logical grammar for Dutch. The annotation scheme used in Tiger is an ex- tension of that used in the earlier, and smaller, German Negra corpus (Skut et al., 1997). Tiger is better suited for the extraction of subcatego- rization information (and thus the translation into “deep” grammars of any kind), since it distin- guishes between PP complements and modifiers, and includes “secondary” edges to indicate shared arguments in coordinate constructions. Tiger also includes morphology and lemma information. Negra is also provided with a “Penn Treebank”- style representation, which uses …

Content truncated to 500 words