期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：Paraphrase generation is an important task
that has received a great deal of interest recently.
Proposed data-driven solutions to the
problem have ranged from simple approaches
that make minimal use of NLP tools to more
complex approaches that rely on numerous
language-dependent resources. Despite all of
the attention, there have been very few direct
empirical evaluations comparing the merits of
the different approaches. This paper empirically
examines the tradeoffs between simple
and sophisticated paraphrase harvesting approaches
to help shed light on their strengths
and weaknesses. Our evaluation reveals that
very simple approaches fare surprisingly well
and have a number of distinct advantages, including
strong precision, good coverage, and
low redundancy.
