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Combining rule-based and statistical mechanisms for low-resource named entity recognition.

, , , , and . Mach. Transl., 32 (1-2): 31-43 (2018)

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MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition., , , , , , , , , and 35 other author(s). EMNLP, page 4488-4508. Association for Computational Linguistics, (2022)Recession Segmentation: Simpler Online Word Segmentation Using Limited Resources., and . CoNLL, page 88-97. ACL, (2010)If You Build Your Own NER Scorer, Non-replicable Results Will Come., and . Insights, page 94-99. Association for Computational Linguistics, (2020)Overview of ADoBo 2021: Automatic Detection of Unassimilated Borrowings in the Spanish Press., , , , and . CoRR, (2021)ParaNames: A Massively Multilingual Entity Name Corpus., and . CoRR, (2022)Modeling Infant Word Segmentation.. CoNLL, page 29-38. ACL, (2011)The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation., and . EACL (Student Research Workshop), page 164-174. Association for Computational Linguistics, (2021)Multilingual Open Text Release 1: Public Domain News in 44 Languages., , and . LREC, page 2080-2089. European Language Resources Association, (2022)Toward More Meaningful Resources for Lower-resourced Languages., , , and . ACL (Findings), page 523-532. Association for Computational Linguistics, (2022)A Rule-Based Acquisition Model Adapted for Morphological Analysis., , , and . CLEF (1), volume 6241 of Lecture Notes in Computer Science, page 658-665. Springer, (2009)