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

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autor(en): Lapesa G, Evert S
Verlagsort: Valencia, Spain
Jahr der Veröffentlichung: 2017
Tagungsband: Proceedings of the 15th Annual Meeting of the European Association for Computational Linguistics (EACL 2017): Volume 2, Short Papers
Seitenbereich: 394-400
Sprache: Englisch


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.



FAU-Autoren / FAU-Herausgeber

Evert, Stefan Prof. Dr.
Professur für Korpuslinguistik
Lapesa, Gabriella
Professur für Korpuslinguistik


Zitierweisen

APA:
Lapesa, G., & Evert, S. (2017). Large-scale evaluation of dependency-based DSMs: Are they worth the effort? In Proceedings of the 15th Annual Meeting of the European Association for Computational Linguistics (EACL 2017): Volume 2, Short Papers (pp. 394-400). Valencia, Spain.

MLA:
Lapesa, Gabriella, and Stefan Evert. "Large-scale evaluation of dependency-based DSMs: Are they worth the effort?" Proceedings of the Proceedings of the 15th Annual Meeting of the European Association for Computational Linguistics (EACL 2017): Volume 2, Short Papers Valencia, Spain, 2017. 394-400.

BibTeX: 

Zuletzt aktualisiert 2018-17-05 um 07:09