Paper: Modelling Semantic Role Pausibility In Human Sentence Processing

ACL ID E06-1044
Title Modelling Semantic Role Pausibility In Human Sentence Processing
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We present the psycholinguistically moti- vatedtask ofpredictinghuman 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 com- pare it with a standard role labeller. For both tasks, our model benefits from class- based smoothing, which allows it to make correct argument-specific predictions de- spite a severe sparse data problem. The standard labeller suffers from sparse data and a strong reliance on syntactic cues, es- pecially in the prediction task.