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
- Editors:
- Lucia Specia, Matt Post, Michael Paul
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D17-2018.pdf
- Data
- Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison