119 citations · 252 across the 17 of their papers we have counts for
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cs.CL2023★ 9 cited
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors
Peng Li, Tianxiang Sun, Qiong Tang +4
Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning ability on many NLP tasks. A common practice is to recast the task into a…
cs.CL2023★ 3 cited
HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction
Yijun Wang, Changzhi Sun, Yuanbin Wu +3
Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-…
cs.CL2023
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Limao Xiong, Jie Zhou, Qunxi Zhu +7
Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unifie…