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26 Silvia Hansen-Schirratreebank projects for other languages have come to life as well, e.g., for French(Abeillé et al. 2000), Italian (Bosco et al. 2000), Spanish (Moreno et al. 2000), etc.For German, the Verbmobil Treebank (Hinrichs et al. 2000) and the TübingenTreebank (Telljohann et al. 2006) are available. However, they are rather smallas reference work and restricted to spoken language (as in the case of Verbmobil).In contrast, the NEGRA/TiGer corpora (Brants et al. 2003), including 70,000sentences, are the ideal basis for empirical investigations. For their annotation, ahybrid framework is used which combines advantages of dependency grammarand phrase structure grammar. The syntactic structure is represented by a tree.The branches of a tree may cross, allowing the encoding of local and non-localdependencies and eliminating the need for traces. This approach has considerableadvantages for free-word order languages such as German, which show a largevariety of discontinuous constituency types (Skut et al. 1997). The linguistic annotationof each sentence in the TiGer Treebank is represented on a number ofdifferent levels: Information on part-of-speech, morphology and lemmata is encodedin terminal nodes (on the word level). Non-terminal nodes are labelled withphrase categories. The edges of a tree represent syntactic functions. Syntactic structuresare rather flat and simple in order to reduce the potential for attachmentambiguities. The distinction between arguments and adjuncts, for instance, is notexpressed in the constituent structure, but is instead encoded by means of syntacticfunctions. Secondary edges, i.e., labelled directed arcs between arbitrary nodes,are used to encode coordination information.Instead of having an automatic parser as pre-processor and a human annotatoras postprocessor (as in the Penn Treebank project), interactive annotationwith the annotation tool (Brants & Plaehn 2000) is used for the annotation process,efficiently combining automatic parsing and human annotation. The TnTtagger (Brants 2000) and the parser using Cascaded Markov Models (Brants 1999)generate small parts of the annotation, which are immediately presented visuallyto the human annotator, who can either accept, correct or reject it. Based on theannotator’s decision, the parser proposes the next part of the annotation, whichis again submitted to the annotator’s judgement. This process is repeated until theannotation of the sentence is complete. The advantage of this interactive method isthat the human decisions can be used by the automatic parser. Thus, errors madeby the automatic parser at lower levels are corrected instantly and do not ‘shinethrough’ on higher levels. The chances grow that the automatic parser proposescorrect analyses on higher levels. In order to achieve a high level of consistencyand to avoid mistakes, we use a very thorough approach to the annotation: First,each sentence is annotated independently by two annotators. With the support ofscripts, they afterwards compare their annotations and correct obvious mistakes.Remaining differences are submitted to a discussion between the annotators. Al-