期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2006
卷号：2006
出版社：ACL Anthology
摘要：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.
