Document-Level Relation Extraction with Cross-sentence Reasoning Graph

Liu, Hongfei; Zhao, Kang; Zhang, Lizong; Liu, Tian; Hua, Fujun

doi:10.1007/978-3-031-33374-3_25

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Internal knowledge-driven methods utilise a graph to model the semantic relationships within the news content and social context. This works in a way similar to some text classification methods [55]. In this model, the news article including comments from other users is regarded as a text document.…”

Section: Overview Of Graph-based Fake News Detection Methodsmentioning

confidence: 99%

Fake News Detection through Graph-based Neural Networks: A Survey

Gong,

Sinnott,

et al. 2023

Preprint

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The popularity of online social networks has enabled rapid dissemination of infor- mation. People now can share and consume information much more rapidly than ever before. However, low-quality and/or accidentally/deliberately fake information can also spread rapidly. This can lead to considerable and negative impacts on society. Identifying, labelling and debunking online misinformation as early as possible has become an increasingly urgent problem. Many methods have been proposed to detect fake news including many deep learning and graph-based approaches. In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news. In this paper, we present a systematic review of fake news detection studies based on graph-based and deep learning-based techniques. We classify existing graph-based methods into knowledge-driven methods, propagation-based methods, and heterogeneous social context-based methods, depending on how a graph structure is constructed to model news related information flows. We further discuss the challenges and open problems in graph-based fake news detection and identify future research directions.

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Section: Overview Of Graph-based Fake News Detection Methodsmentioning

confidence: 99%

Fake News Detection through Graph-based Neural Networks: A Survey

Gong,

Sinnott,

et al. 2023

Preprint

View full text Add to dashboard Cite

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Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph

Thi

Dao

Nguyen

et al. 2023

Int J Comput Intell Syst

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Identifying chemical-induced disease (CID) semantic relations in the biomedical literature, including both intra- and inter-sentence interactions, has significant implications for various downstream applications. Although various advanced methods have been proposed, they often overlook the cross-sentence dependency information, which is crucial for accurately predicting inter-sentence relations. In this study, we propose DEGREx, a novel graph-based neural model that presents a biomedical document as a dependency graph. DEGREx improves the long-distance relation extraction by allowing direct information exchange among document graph nodes through dependency connections. The information transition process is based on the idea of controller gates in long short-term memory networks. Our model, DEGREx, exerts a multi-task learning framework to jointly train relation extraction with named entity recognition, improving the performance of the CID extraction task. Experimental results on the benchmark dataset demonstrate that our model DEGREx outperforms all nine compared recent state-of-the-art models.

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