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- W3103674997 abstract "Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-the-art baselines." @default.
- W3103674997 created "2020-11-23" @default.
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- W3103674997 date "2020-01-01" @default.
- W3103674997 modified "2023-09-26" @default.
- W3103674997 title "Hierarchical Region Learning for Nested Named Entity Recognition" @default.
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- W3103674997 doi "https://doi.org/10.18653/v1/2020.findings-emnlp.430" @default.
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