期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：Current approaches to the prediction of
associations rely on just one type of information,
generally taking the form of
either word space models or collocation
measures. At the moment, it is an open
question how these approaches compare
to one another. In this paper, we will
investigate the performance of these two
types of models and that of a new approach
based on compounding. The best
single predictor is the log-likelihood ratio,
followed closely by the document-based
word space model. We will show, however,
that an ensemble method that combines
these two best approaches with the
compounding algorithm achieves an increase
in performance of almost 30% over
the current state of the art.
