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Evaluating machine-assisted annotation in under-resourced settings.

, , , , , and . Lang. Resour. Evaluation, 48 (4): 561-599 (2014)

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Why multiple document image binarizations improve OCR., , and . HIP@ICDAR, page 86-93. ACM, (2013)An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning., , , and . LAW@NAACL-HLT, page 11-20. The Association for Computer Linguistics, (2015)French Amalgam: A machine-learned sentence realization system., , , and . TALN (Articles longs), page 245-254. ATALA, (2003)Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings., , and . COLING, page 1787-1796. ACL, (2016)Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types., , , and . COLING, page 1694-1704. Association for Computational Linguistics, (2018)A Probabilistic Morphological Analyzer for Syriac., , , , , , and . EMNLP, page 810-820. ACL, (2010)Rapid language model development for new task domains., , and . LREC, page 807-814. European Language Resources Association, (1998)A synthetic document image dataset for developing and evaluating historical document processing methods., , and . DRR, volume 8297 of SPIE Proceedings, page 829710. SPIE, (2012)How well does multiple OCR error correction generalize?, , and . DRR, volume 9021 of SPIE Proceedings, page 90210A-90210A-13. SPIE, (2014)Cliff Walls: An Analysis of Monolithic Commits Using Latent Dirichlet Allocation., , , and . OSS, volume 365 of IFIP Advances in Information and Communication Technology, page 282-298. Springer, (2011)