Search Results for author:
Found 12 papers, 6 papers with code
On Detecting Cherry-picking in News Coverage Using Large Language Models
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
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
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.
Modeling Factual Claims with Semantic Frames
In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims.
A Benchmark Dataset of Check-worthy Factual Claims
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.
Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study
A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world.
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
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.
Relaxing Relationship Queries on Graph Data
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
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.
ClaimPortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on Twitter
We present ClaimPortal, a web-based platform for monitoring, searching, checking, and analyzing English factual claims on Twitter from the American political domain.
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
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.
Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons
This design is both sufficient and efficient, as it is proven to find a short terminal question sequence.
