Semantic Role Labeling with the Swedish FrameNet
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.
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