期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：394-400
语种：English
出版社：ACL Anthology
摘要：This paper presents a large-scale evaluation study of dependency-based distributional semantic models. We evaluate dependency-filtered and dependency-structured DSMs in a number of standard semantic similarity tasks, systematically exploring their parameter space in order to give them a “fair shot” against window-based models. Our results show that properly tuned window-based DSMs still outperform the dependency-based models in most tasks. There appears to be little need for the language-dependent resources and computational cost associated with syntactic analysis.
