Abstract
We describe the features of SeqScore, an MIT-licensed Python toolkit for working with named entity recognition (NER) data.While SeqScore began as a tool for NER scoring, it has been expanded to help with the full lifecycle of working with NER data: validating annotation, providing at-a-glance and detailed summaries of the data, modifying annotation to support experiments, scoring system output, and aiding with error analysis.SeqScore is released via PyPI (https://pypi.org/project/seqscore/) and development occurs on GitHub (https://github.com/bltlab/seqscore).- Anthology ID:
- 2023.nlposs-1.17
- Volume:
- Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, Elijah Rippeth
- Venues:
- NLPOSS | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 147–152
- Language:
- URL:
- https://aclanthology.org/2023.nlposs-1.17
- DOI:
- 10.18653/v1/2023.nlposs-1.17
- Cite (ACL):
- Constantine Lignos, Maya Kruse, and Andrew Rueda. 2023. Improving NER Research Workflows with SeqScore. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 147–152, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Improving NER Research Workflows with SeqScore (Lignos et al., NLPOSS-WS 2023)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2023.nlposs-1.17.pdf