30 citations · 32 across the 2 of their papers we have counts for
3 papers
cs.CL2020★ 2 cited
A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss
Shanchan Wu, Kai Fan
Either human annotation or rule based automatic labeling is an effective method to augment data for relation extraction. However, the inevitable wrong labeling problem for example…
cs.CL2019★ 30 cited
Enriching Pre-trained Language Model with Entity Information for Relation Classification
Shanchan Wu, Yifan He
Relation classification is an important NLP task to extract relations between entities. The state-of-the-art methods for relation classification are primarily based on Convolutiona…
cs.CL2018
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector
Shanchan Wu, Kai Fan, Qiong Zhang
Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervis…