119 citations · 240 across the 14 of their papers we have counts for
12 papers · 1 filter
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…
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-…
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…
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition
Hao Zhou, Man Lan, Yuanbin Wu +2
Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted mu…
Few Clean Instances Help Denoising Distant Supervision
Yufang Liu, Ziyin Huang, Yijun Wang +5
Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate…
A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation
Shen Liu, Meirong Ma, Hao Yuan +3
Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different m…