The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD

Eneko Agirre, Oier López de Lacalle, Aitor Soroa


Abstract
UKB is an open source collection of programs for performing, among other tasks, Knowledge-Based Word Sense Disambiguation (WSD). Since it was released in 2009 it has been often used out-of-the-box in sub-optimal settings. We show that nine years later it is the state-of-the-art on knowledge-based WSD. This case shows the pitfalls of releasing open source NLP software without optimal default settings and precise instructions for reproducibility.
Anthology ID:
W18-2505
Volume:
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Eunjeong L. Park, Masato Hagiwara, Dmitrijs Milajevs, Liling Tan
Venue:
NLPOSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
29–33
Language:
URL:
https://aclanthology.org/W18-2505
DOI:
10.18653/v1/W18-2505
Bibkey:
Cite (ACL):
Eneko Agirre, Oier López de Lacalle, and Aitor Soroa. 2018. The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS), pages 29–33, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD (Agirre et al., NLPOSS 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2505.pdf