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FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space
[article]
2022
arXiv
pre-print
Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context. While distant supervision is the most common way to acquire supervised training data, it brings in label noise, as it assigns type labels to the entity mentions irrespective of mentions context. In attempts to deal with the label noise, leading research on the FG-NET assumes that the fine-grained entity typing data possesses a
arXiv:2101.11212v3
fatcat:atnenkdwanekbizti3h2pvf2ja