期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：This paper presents a new, exemplar-based
model of thematic fit. In contrast to previous
models, it does not approximate
thematic fit as argument plausibility or
‘fit with verb selectional preferences’, but
directly as semantic role plausibility for
a verb-argument pair, through similaritybased
generalization from previously seen
verb-argument pairs. This makes the
model very robust for data sparsity. We
argue that the model is easily extensible to
a model of semantic role ambiguity resolution
during online sentence comprehension.
The model is evaluated on human semantic
role plausibility judgments. Its predictions
correlate significantly with the human
judgments. It rivals two state-of-theart
models of thematic fit and exceeds their
performance on previously unseen or lowfrequency
items.
