Improving NER Research Workflows with SeqScore

Constantine Lignos, Maya Kruse, Andrew Rueda


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2023.nlposs-1.17.pdf
Video:
 https://aclanthology.org/2023.nlposs-1.17.mp4