Towards a Resource for Lexical Semantics: A Large German Corpus with Extensive Semantic Annotation

Towards a Resource for Lexical Semantics: A Large German Corpus with Extensive Semantic Annotation Katrin Erk and Andrea Kowalski and Sebastian Pad´o and Manfred Pinkal Department of Computational Linguistics Saarland University Saarbr¨ucken, Germany {erk, kowalski, pado, pinkal}@coli.uni-sb.de Abstract We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large- scale acquisition of word-semantic infor- mation, e.g. the construction of domain- independent lexica. The backbone of the annotation are semantic roles in the frame semantics paradigm. We report expe- riences and evaluate the annotated data from the first project stage. On this ba- sis, we discuss the problems of vagueness and ambiguity in semantic annotation. 1 Introduction Corpus-based methods for syntactic learning and processing are well-established in computational linguistics. There are comprehensive and carefully worked-out corpus resources available for a num- ber of languages, e.g. the Penn Treebank (Marcus et al., 1994) for English or the NEGRA corpus (Skut et al., 1998) for German. In semantics, the sit- uation is different: Semantic corpus annotation is only in its initial stages, and currently only a few, mostly small, corpora are available. Semantic an- notation has predominantly concentrated on word senses, e.g. in the SENSEVAL initiative (Kilgarriff, 2001), a notable exception being the Prague Tree- bank (Hajiˇcov´a, 1998) . As a consequence, most recent work in corpus-based semantics has taken an unsupervised approach, relying on statistical meth- ods to extract semantic regularities from raw cor- pora, often using information from ontologies like WordNet (Miller et al., 1990). Meanwhile, the lack of large, domain- independent lexica providing word-semantic information is one of the most serious bottlenecks for language technology. To train tools for the acquisition of semantic information for such lexica, large, extensively annotated resources are necessary. In this paper, we present current work of the SALSA (SAarbr¨ucken Lexical Semantics Annota- tion and analysis) project, whose aim is to provide such a resource and to investigate efficient methods for its utilisation. In the current project phase, the focus of our research and the backbone of the an- notation are semantic role relations. More specif- ically, our role annotation is based on the Berke- ley FrameNet project (Baker et al., 1998; Johnson et al., 2002). In addition, we selectively annotate word senses and anaphoric links. The TIGER corpus (Brants et al., 2002), a 1.5M word German newspa- per corpus, serves as sound syntactic basis. Besides the sparse data problem, the most seri- ous problem for corpus-based lexical semantics is the lack of specificity of the data: Word meaning is notoriously ambiguous, vague, and subject to con- textual variance. The problem has been recognised and discussed in connection with the SENSEVAL task (Kilgarriff and Rosenzweig, 2000). Annotation of frame semantic roles compounds the problem as it combines word sense assignment with the assign- ment of semantic roles, a task that introduces vague- ness and ambiguity problems of its own. The problem can be alleviated by choosing a suit- able resource as annotation basis. FrameNet roles, which are local to …

Content truncated to 500 words