Large-scale evaluation of dependency-based DSMs: Are they worth the effort?

Gabriella Lapesa, Stefan Evert


Abstract
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.
Anthology ID:
E17-2063
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
394–400
Language:
URL:
https://aclanthology.org/E17-2063
DOI:
Bibkey:
Cite (ACL):
Gabriella Lapesa and Stefan Evert. 2017. Large-scale evaluation of dependency-based DSMs: Are they worth the effort?. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 394–400, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Large-scale evaluation of dependency-based DSMs: Are they worth the effort? (Lapesa & Evert, EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-2063.pdf