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 a model for sentence compression
that uses a discriminative largemargin
learning framework coupled with
a novel feature set defined on compressed
bigrams as well as deep syntactic representations
provided by auxiliary dependency
and phrase-structure parsers. The
parsers are trained out-of-domain and contain
a significant amount of noise. We argue
that the discriminative nature of the
learning algorithm allows the model to
learn weights relative to any noise in the
feature set to optimize compression accuracy
directly. This differs from current
state-of-the-art models (Knight and
Marcu, 2000) that treat noisy parse trees,
for both compressed and uncompressed
sentences, as gold standard when calculating
model parameters.
