Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction

Sheng, Jiabao; Lam, Saikit; Li, Z.; Zhang, Jiang; Teng, Xinzhi; Zhang, Yuanpeng; Cai, Jing

doi:10.1145/3591106.3592261

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…[16] investigated the coherence of networks with recursive features and proposed a class of nested weighted n-polygon networks; Ref. [17][18][19] has carried out a lot of research on cross-domain network structures based on contrastive learning, and applying these methods in the field of aspect-level sentiment analysis is also a great breakthrough; In Ref. [20][21][22], cross-domain network structures are used to conduct a large number of studies on text classification tasks when there are few corpora.…”

Section: Introductionmentioning

confidence: 99%

GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

Wang

et al. 2023

Symmetry

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Currently, attention mechanisms are widely used in aspect-level sentiment analysis tasks. Previous studies have only used attention mechanisms combined with neural networks for aspect-level sentiment classification, and the feature extraction of the model is insufficient. When the same aspect and sentiment polarity appear in multiple sentences, the semantic information sharing of the same domain is also ignored, resulting in low model performance. To address these problems, the paper proposes an aspect-level sentiment analysis model, GCAT-GTCU, which combines a Graph-connected Attention Network containing symmetry with Gate Than Change Unit. Three nodes of words, sentences, and aspects are constructed, and local and deep-level features of sentences are extracted using CNN splicing BiGRU; node connection information is added to GAT to form a GCAT containing symmetry to realize the information interaction of three nodes, pay attention to the contextual information, and update the shared information of three nodes at any time; a new gating mechanism GTCU is constructed to filter noisy information and control the flow of sentiment information; finally, the three nodes are extracted information to predict the final sentiment polarity. The experimental results on four publicly available datasets show that the model outperforms the baseline model against which it is compared in some very controlled situations.

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Section: Introductionmentioning

confidence: 99%

GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

Wang

et al. 2023

Symmetry

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning

Zhou,

Li,

et al. 2024

Lecture Notes in Computer Science

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Multimodal Rumor Detection by Using Additive Angular Margin with Class-Aware Attention for Hard Samples

Zhou,

Li,

et al. 2023

Lecture Notes in Computer Science

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Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction

Cited by 7 publications

References 13 publications

GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning

Multimodal Rumor Detection by Using Additive Angular Margin with Class-Aware Attention for Hard Samples

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