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
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 394–400
- Language:
- URL:
- https://aclanthology.org/E17-2063
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/E17-2063.pdf