Abstract
This paper presents the results of a large-scale evaluation study of window-based Distributional Semantic Models on a wide variety of tasks. Our study combines a broad coverage of model parameters with a model selection methodology that is robust to overfitting and able to capture parameter interactions. We show that our strategy allows us to identify parameter configurations that achieve good performance across different datasets and tasks.- Anthology ID:
- Q14-1041
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 2
- Month:
- Year:
- 2014
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 531–546
- Language:
- URL:
- https://aclanthology.org/Q14-1041
- DOI:
- 10.1162/tacl_a_00201
- Cite (ACL):
- Gabriella Lapesa and Stefan Evert. 2014. A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection. Transactions of the Association for Computational Linguistics, 2:531–546.
- Cite (Informal):
- A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection (Lapesa & Evert, TACL 2014)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/Q14-1041.pdf