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Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.

, , , , and . Mach. Learn., 109 (9-10): 1749-1778 (2020)

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Clustering-based Inference for Zero-Shot Biomedical Entity Linking., , , , and . CoRR, (2020)Clustering-based Inference for Biomedical Entity Linking., , , , and . NAACL-HLT, page 2598-2608. Association for Computational Linguistics, (2021)Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs., , , and . BioNLP, page 222-231. Association for Computational Linguistics, (2017)Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition., , , , and . CoRR, (2019)Using error decay prediction to overcome practical issues of deep active learning for named entity recognition., , , , and . Mach. Learn., 109 (9-10): 1749-1778 (2020)Low resource recognition and linking of biomedical concepts from a large ontology., , , and . BCB, page 54:1-54:10. ACM, (2021)Automatic Construction of a Hierarchical Generate-and-Test Algorithm., and . ML, page 483-484. Morgan Kaufmann, (1989)LSTM-RASA Based Agri Farm Assistant for Farmers., , , , , and . CoRR, (2022)A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes., , , and . LREC, page 1073-1082. European Language Resources Association, (2022)A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval., , , and . WWW, page 77-86. ACM, (2018)