370 citations · 1.5k across the 61 of their papers we have counts for
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Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
Juan Li, Ruoxu Wang, Ningyu Zhang +3
Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occu…
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can gras…
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Haiyang Yu, Ningyu Zhang, Shumin Deng +3
Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance. Exis…
Finding Influential Instances for Distantly Supervised Relation Extraction
Zifeng Wang, Rui Wen, Xi Chen +3
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise. Current works based on attent…
Contrastive Triple Extraction with Generative Transformer
Hongbin Ye, Ningyu Zhang, Shumin Deng +4
Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple e…
Conceptualized Representation Learning for Chinese Biomedical Text Mining
Ningyu Zhang, Qianghuai Jia, Kangping Yin +3
Biomedical text mining is becoming increasingly important as the number of biomedical documents and web data rapidly grows. Recently, word representation models such as BERT has ga…