Search Results for author: Chengkai Li

Found 12 papers, 6 papers with code

On Detecting Cherry-picking in News Coverage Using Large Language Models

1 code implementation11 Jan 2024 Israa Jaradat, Haiqi Zhang, Chengkai Li

This study introduces Cherry, an innovative approach for automatically detecting cherry-picked statements in news articles by finding missing important statements in the target news story.

A Dashboard for Mitigating the COVID-19 Misinfodemic

no code implementations EACL 2021 Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohanmmed Samiul Saeef, Paras Pathak, Chengkai Li

This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter.


Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation

no code implementations ACL 2020 Yunyao Li, Gr, Tyrone ison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, Haiqi Zhang

Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread.

Chatbot Misinformation

Modeling Factual Claims with Semantic Frames

no code implementations LREC 2020 Fatma Arslan, Josue Caraballo, Damian Jimenez, Chengkai Li

In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims.

Fact Checking

A Benchmark Dataset of Check-worthy Factual Claims

no code implementations29 Apr 2020 Fatma Arslan, Naeemul Hassan, Chengkai Li, Mark Tremayne

In this paper we present the ClaimBuster dataset of 23, 533 statements extracted from all U. S. general election presidential debates and annotated by human coders.

Fact Checking

Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

1 code implementation18 Mar 2020 Farahnaz Akrami, Mohammed Samiul Saeef, Qingheng Zhang, Wei Hu, Chengkai Li

A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world.

Knowledge Graph Completion Link Prediction

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs

1 code implementation10 Mar 2020 Zequn Sun, Qingheng Zhang, Wei Hu, Chengming Wang, Muhao Chen, Farahnaz Akrami, Chengkai Li

Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings.

Benchmarking Entity Alignment +1

Relaxing Relationship Queries on Graph Data

1 code implementation24 Feb 2020 Shuxin Li, Gong Cheng, Chengkai Li

We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities.

Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims

1 code implementation18 Feb 2020 Kevin Meng, Damian Jimenez, Fatma Arslan, Jacob Daniel Devasier, Daniel Obembe, Chengkai Li

We present a study on the efficacy of adversarial training on transformer neural network models, with respect to the task of detecting check-worthy claims.

text-classification Text Classification

ClaimPortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on Twitter

no code implementations ACL 2019 Sarthak Majithia, Fatma Arslan, Sumeet Lubal, Damian Jimenez, Priyank Arora, Josue Caraballo, Chengkai Li

We present ClaimPortal, a web-based platform for monitoring, searching, checking, and analyzing English factual claims on Twitter from the American political domain.

Fact Checking

Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

1 code implementation16 Aug 2017 Zequn Sun, Wei Hu, Chengkai Li

Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.

Attribute Entity Alignment +2

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