SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation

Simone Papandrea, Alessandro Raganato, Claudio Delli Bovi


Abstract
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD.
Anthology ID:
D17-2018
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–108
Language:
URL:
https://aclanthology.org/D17-2018
DOI:
10.18653/v1/D17-2018
Bibkey:
Cite (ACL):
Simone Papandrea, Alessandro Raganato, and Claudio Delli Bovi. 2017. SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 103–108, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation (Papandrea et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-2018.pdf
Code
 SI3P/SupWSD
Data
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison