Modelling Semantic Role Pausibility in Human Sentence Processing

Abstract

We present the psycholinguistically motivated task of predicting human plausibility judgements for verb-role-argument triples and introduce a probabilistic model that solves it. We also evaluate our model on the related role-labelling task, and compare it with a standard role labeller. For both tasks, our model benefits from classbased smoothing, which allows it to make correct argument-specific predictions despite a severe sparse data problem. The standard labeller suffers from sparse data and a strong reliance on syntactic cues, especially in the prediction task.

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Cite this paper

@inproceedings{Pad2006ModellingSR, title={Modelling Semantic Role Pausibility in Human Sentence Processing}, author={Ulrike Pad{\'o} and Matthew W. Crocker and Frank Keller}, booktitle={EACL}, year={2006} }