Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities (N19-1)
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Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, Marie-Catherine de Marneffe
| Challenge: | Scholars in interdisciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. |
| Approach: | They propose an active learning solution for named entity recognition that maximizes a custom model’s improvement per additional unit of manual annotation. |
| Outcome: | The proposed model reduces required annotation by 20-60% and outperforms a competitive active learning baseline. |
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