This paper presents a large-scale evaluation study of dependency-based distributional semantic models. We evaluate dependencyfiltered 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 dependencybased models in most tasks. There appears to be little need for the language-dependent resources and computational cost associated with syntactic analysis. 1
REF evaluate the performance of dependency-based and simpler window-based models for computing semantic similarity and find the simpler model to be superior in most cases.
Large-scale evaluation of dependency-based DSMs: Are they worth the effort?
EACL
Computer Science