Titlebook: Social Media Processing; 11th Chinese Nationa Feng Wu,Xuanjing Huang,Jing Zhang Conference proceedings 2024 The Editor(s) (if applicable) a

Peristalsis

commodity

,Leverage Heterogeneous Graph Neural Networks for Short-Text Conceptualization,ovide a flexible and natural modeling tool to model such complex relationships and capture more expressive and discriminative concepts, by leveraging mutually reinforcing strategy on heterogeneous correlations. On the other word, it is a beneficial attempt to introduce different types of semantic no

Oligarchy

高深莫测

,What You Write Represents Your Personality: A Dual Knowledge Stream Graph Attention Network for Peromprises of two streams. One stream represents posts at the psycholinguistic level, while the other encodes words at a more finely-grained emotional level based on prior emotional knowledge. Both streams’ representations are then obtained to make joint inferences about personality traits. Our approa

Evocative

,Detect Depression from Social Networks with Sentiment Knowledge Sharing,ting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.

Overdose

助记

,CDBMA: Community Detection in Heterogeneous Networks Based on Multi-attention Mechanism, of the network. The semantic information encoder uses node attention to learn the importance of high-order neighbor nodes based on meta-paths, uses semantic attention to learn the weights of different meta-paths, and fuses the content semantic information on different meta-paths to learn the conten

启发

An Adaptive Denoising Recommendation Algorithm for Causal Separation Bias,tion, we separate the confounding of users’ conformity and interest in the selection bias by causal inference. Specifically, we construct a multi-task learning model with regularization loss functions. Experimental results on the two datasets demonstrate the superiority of our ADA model over state-o

发展

Enzyme

,Retrieval-Augmented Document-Level Event Extraction with Cross-Attention Fusion,m. Experimental results obtained from a comprehensive evaluation of a large-scale document-level event extraction dataset reveal that our proposed method surpasses the performance of all baseline models. Furthermore, our approach exhibits improved performance even in low-resource settings, emphasizi