Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
arXiv preprint arXiv:2203.03903 , year=
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GLiNER-Relex unifies NER and RE in one zero-shot transformer-based model that achieves competitive results on CoNLL04, DocRED, FewRel, and CrossRE.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
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GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction
GLiNER-Relex unifies NER and RE in one zero-shot transformer-based model that achieves competitive results on CoNLL04, DocRED, FewRel, and CrossRE.