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Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs.

, , и . EMNLP/IJCNLP (1), стр. 4924-4935. Association for Computational Linguistics, (2019)

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The ATRACT Workbench: Automatic Term Recognition and Clustering for Terms., , и . TSD, том 2166 из Lecture Notes in Computer Science, стр. 126-133. Springer, (2001)Inferring appropriate eligibility criteria in clinical trial protocols without labeled data., и . DTMBIO@CIKM, стр. 21-28. ACM, (2012)A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience., , , , , , и . Neuroinformatics, 17 (3): 391-406 (2019)Global information-aware argument mining based on a top-down multi-turn QA model., , , , и . Inf. Process. Manag., 60 (5): 103445 (сентября 2023)Rethinking Large Language Models in Mental Health Applications., , , , и . CoRR, (2023)ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials., , и . BMC Medical Informatics Decis. Mak., 12 (S-1): S3 (2012)Using automatically learnt verb selectional preferences for classification of biomedical terms., и . J Biomed Inform, 37 (6): 483--497 (декабря 2004)biochem4j: Integrated and extensible biochemical knowledge through graph databases., , , , , , , , , и 4 other автор(ы). PloS one, (2017)Learning to recognise named entities in tweets by exploiting weakly labelled data., , и . NUT@COLING, стр. 153-163. The COLING 2016 Organizing Committee, (2016)A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge., , , и . CIKM, стр. 2917-2927. ACM, (2023)