Search Results for author:
Found 10 papers, 7 papers with code
Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
To address this, we propose a novel word-level Heterogeneous-graph-based model for Fact Checking over unstructured and structured information, namely HeterFC.
Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment
In this paper, we present Starling, an I/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment.
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
Each instance is accompanied by a veracity label and an explanation that outlines the reasoning path supporting the veracity classification.
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Comprehensive experiments have demonstrated the superiority of GETRAL over the state-of-the-arts and validated the efficacy of semantic mining with graph structure and contrastive learning.
Application of Data Encryption in Chinese Named Entity Recognition
Recently, with the continuous development of deep learning, the performance of named entity recognition tasks has been dramatically improved.
Chinese Named Entity Recognition
named-entity-recognition
+1
Evidence-aware Fake News Detection with Graph Neural Networks
In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i. e., a claim).
A Survey on Graph Structure Learning: Progress and Opportunities
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level.
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
To this end, we present a minimax selection scheme that explicitly harnesses neighborhood information and discover homophilous subgraphs to facilitate active selection.
Accelerating convolutional neural network by exploiting sparsity on GPUs
Multiplications and additions for zero values in the feature map are useless for convolution results.
Distributed, Parallel, and Cluster Computing
