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dc.contributor.authorErdmann, Alexander-
dc.contributor.authorWrisley, David Joseph-
dc.contributor.authorBrown, Christopher-
dc.contributor.authorCohen-Bodénès, Sophie-
dc.contributor.authorElsner, Micha-
dc.contributor.authorFeng, Yukun-
dc.contributor.authorJoseph, Brian-
dc.contributor.authorJoyeux-Prunel, Béatrice-
dc.contributor.authorde Marneffe, Marie-Catherine-
dc.identifier.citationErdmann, A. et al. (2019) Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities. Proceedings of NAACL-HLT 2019, pages 2223–2234 Minneapolis, Minnesota, June 2 - June 7, 2019.en
dc.description.abstractScholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. Yet, under-resourced languages, imperfect or noisily structured data, and user-specific classification tasks make it difficult to meet their needs using off-the-shelf models. Manual annotation of large corpora from scratch, meanwhile, can be prohibitively expensive. Thus, we propose an active learning solution for named entity recognition, attempting to maximize a custom model’s improvement per additional unit of manual annotation. Our system robustly handles any domain or user-defined label set and requires no external resources, enabling quality named entity recognition for Humanities corpora where such resources are not available. Evaluating on typologically disparate languages and datasets, we reduce required annotation by 20-60% and greatly outperform a competitive active learning baseline.en
dc.description.sponsorshipNew York University–Paris Sciences Lettres Global Alliance grant; National Endowment for the Humanities grant, award HAA-256078-17; Computational Approaches to Modeling Language lab at New York University Abu Dhabien
dc.publisherAssociation for Computational Linguisticsen
dc.subjectdigital humanitiesen
dc.subjectnamed entity recognitionen
dc.subjectactive learningen
dc.subjectmachine learningen
dc.titlePractical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanitiesen
Appears in Collections:David Wrisley's Collection

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