Improving NER Research Workflows with SeqScore Article Swipe
Related Concepts
Computer science
Annotation
Python (programming language)
Workflow
Named-entity recognition
Information retrieval
Programming language
Software engineering
Natural language processing
Artificial intelligence
Database
Economics
Management
Task (project management)
Constantine Lignos
,
Maya Kruse
,
Andrew Rueda
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.nlposs-1.17
· OA: W4389523699
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.nlposs-1.17
· OA: W4389523699
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).
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