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：Graph-based semi-supervised learning
(SSL) algorithms have been successfully
used to extract class-instance pairs from
large unstructured and structured text collections.
However, a careful comparison
of different graph-based SSL algorithms
on that task has been lacking. We compare
three graph-based SSL algorithms
for class-instance acquisition on a variety
of graphs constructed from different domains.
We find that the recently proposed
MAD algorithm is the most effective. We
also show that class-instance extraction
can be significantly improved by adding
semantic information in the form of
instance-attribute edges derived from
an independently developed knowledge
base. All of our code and data will be
made publicly available to encourage
reproducible research in this area.
