Improving NER Research Workflows with SeqScore
Keywords: Named entity recognition, Data processing, Error analysis
TL;DR: SeqScore is a powerful, MIT-licensed, Python toolkit for working with NER data and it saves researchers from a lot of bugs and one-off scripts.
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).
Submission Number: 25
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