activity
20172020
most citedRumor Detection on Social Media: Datasets, Methods and Opportunities

5 citations · 11 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL20201 cited

SemEval-2020 Task 5: Counterfactual Recognition

Xiaoyu Yang, Stephen Obadinma, Huasha Zhao +3

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances…

cs.IR20195 cited

Rumor Detection on Social Media: Datasets, Methods and Opportunities

Quanzhi Li, Qiong Zhang, Luo Si +1

Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fa…

cs.CL2019

Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization

Yingchi Liu, Quanzhi Li, Marika Cifor +3

The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the \#MeToo and \#TimesUp movements. S…

cs.IR20191 cited

Graph Convolution for Multimodal Information Extraction from Visually Rich Documents

Xiaojing Liu, Feiyu Gao, Qiong Zhang +1

Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, v…

cs.CL20193 cited

Multi-Instance Learning for End-to-End Knowledge Base Question Answering

Mengxi Wei, Yifan He, Qiong Zhang +1

End-to-end training has been a popular approach for knowledge base question answering (KBQA). However, real world applications often contain answers of varied quality for users' qu…

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…