Improving NER Research Workflows with SeqScore Article Swipe

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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

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).