We present the first results on semantic role labeling using the Swedish FrameNet, which is a lexical resource currently in development. Several aspects of the task are investigated, including the %design and selection of machine learning features, the effect of choice of syntactic parser, and the ability of the system to generalize to new frames and new genres. In addition, we evaluate two methods to make the role label classifier more robust: cross-frame generalization and cluster-based features. Although the small amount of training data limits the performance achievable at the moment, we reach promising results. In particular, the classifier that extracts the boundaries of arguments works well for new frames, which suggests that it already at this stage can be useful in a semi-automatic setting.

The Swedish FrameNet project, SweFN, is a lexical resource under development, designed to support both humans and different applications within language technology, such as text generation, text understanding and information extraction. SweFN is constructed in line with the Berkeley FrameNet and the project is aiming to make it a free, full-scale, multi-functional lexical resource covering morphological, syntactic, and semantic descriptions of 50,000 entries. Frames populated by lexical units belonging to the general vocabulary dominate in SweFN, but there are also frames from the medical and the art domain. As Swedish is a language with very productive compounding, special attention is paid to semantic relations within the one word compounds which populate the frames. This is of relevance for understanding the meaning of the compounds and for capturing the semantic and syntactic alternations which are brought about in the course of compounding. SweFN is a component within a complex of modern and historical lexicon resources named SweFN++, available at .