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：Resolving polysemy and synonymy is required
for high-quality information extraction.
We present ConceptResolver, a component for
the Never-Ending Language Learner (NELL)
(Carlson et al., 2010) that handles both phenomena
by identifying the latent concepts that
noun phrases refer to. ConceptResolver performs
both word sense induction and synonym
resolution on relations extracted from text using
an ontology and a small amount of labeled
data. Domain knowledge (the ontology)
guides concept creation by defining a set of
possible semantic types for concepts. Word
sense induction is performed by inferring a set
of semantic types for each noun phrase. Synonym
detection exploits redundant information
to train several domain-specific synonym
classifiers in a semi-supervised fashion. When
ConceptResolver is run on NELL¡¯s knowledge
base, 87% of the word senses it creates correspond
to real-world concepts, and 85% of
noun phrases that it suggests refer to the same
concept are indeed synonyms.
