Automation of Treebank Annotation

I I B i / / / / / / B | / / B B B Automation of Treebank Annotation Thorsten Brants and Wojciech Skut Universit£t des Saarlandes Computational Linguistics D-66041 Saarbrficken, Germany {brant s, skut }Qcoli. uni-sb, de •.. Abstract This paper describes applications of stochastic and symbolic NLP methods to treebank anno- tation. In paxticular we focus on (1) the au- tomation of treebank annotation, (2) the com- parison of conflicting annotations for the same sentence and (3) the automatic detection of in- consistencies. These techniques are currently employed for building a German treebank. 1 Introduction The emergence of new statistical NLP methods in- creases the demand for corpora annotated with syn- tactic structures. The construction of such a cor- pus (a treebank) is a time-consuming task that can hardly be carried out unless some annotation work is automated. Purely automatic annotation, however, is not reliable enough to be employed without some form of human supervision and hand-correction. This interactive annotation strategy requires tools for error detection and consistency checking. The present paper reviews our experience with the development of automatic annotation tools which are currently used for building a corpus of German newspaper text. The next section gives an overview of the annota- tion format. Section 3 describes three applications of statistical NLP methods to treebank annotation. Finally, section 4 discusses mechanisms for compar- ing structures assigned by different annotators. 2 Annotating Argument Structure 2.1 Annotation Scheme Unlike most treebanks of English, our corpus is an- notated with predicate-argumenl s~ructures and not phrase-structure trees. The reason is the free word order in German, a feature seriously affecting the transparency of traditional phrase structures. Thus local and non-local dependencies are represented in Dar'uber PROAV rnu's nachgedacht werden VMFIN VVPP VAINF must thought-over be 'it has to be thought over' about-it $. Figure h Sample structure from the Treebank the same way, at the cost of allowing crossing tree branches, as shown in figure 11 . Such a direct representation of the predicate- argument relation makes annotation easier than it would be if additional trace-filler co-references were used for encoding discontinuous constituents. Fur- thermore, our scheme facilitates automatic extrac- tion of valence frames and the construction of se- mantic representations. On the other hand, the predicate-argument struc- tures used for annotating our corpus can still be con- verted automatically into phrase-structure trees if necessary, cf. (Skut et al., 1997a). For more details on the annotation scheme v. (Skut et al., 1997b). 2.2 The Annotation Mode In order to make annotation more reliable, each sen- tence is annotated independently by two annotators. Afterwards, the results are compared, and both an- notators have to agree on a unique structure. In 1 The nodes and edges are labeled with category and function symbols, respectively (see appendix A). Brants and Skut 49 Automation of Treebank Annotation Thorsten Brants and Wojciech Skut (1998) Automation of Treebank Annotation. In D.M.W. Powers (ed.) NeMLaP3/CoNLL98: New Methods in Language Processing and Computational Natural Language Learning, ACL, …

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