期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：This paper proposes a convolution forest kernel
to effectively explore rich structured features
embedded in a packed parse forest. As
opposed to the convolution tree kernel, the
proposed forest kernel does not have to commit
to a single best parse tree, is thus able to
explore very large object spaces and much
more structured features embedded in a forest.
This makes the proposed kernel more robust
against parsing errors and data sparseness issues
than the convolution tree kernel. The paper
presents the formal definition of convolution
forest kernel and also illustrates the computing
algorithm to fast compute the proposed
convolution forest kernel. Experimental results
on two NLP applications, relation extraction
and semantic role labeling, show that the proposed
forest kernel significantly outperforms
the baseline of the convolution tree kernel.
