Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-Based Sentiment Analysis

Wang, Junlang; Li, Xia; He, Junyi; Zheng, Yongqiang; Ma, Junteng

doi:10.1007/978-981-99-6207-5_24

Cited by 1 publication

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Phan and Ogunbona (2020) proposed a variant of LCF that uses syntactic relative distance over the dependency tree. With the rise of pre-trained language models (PLMs) and graph neural networks (GNNs), more and more studies adopt PLMs (such as BERT (Devlin et al 2019), RoBERTa (Liu et al 2019)) to encode semantic representations and employ GNNs (such as GCN (Kipf and Welling 2017), GAT (Veličković et al 2018)) to fuse syntax features (Zhou et al 2021;Li et al 2021;Tang et al 2022;Zhang, Zhou, and Wang 2022;Liu et al 2022;Wang et al 2023;Zheng, Li, and Nie 2023).…”

Section: Related Workmentioning

confidence: 99%

You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification

Zheng,

2024

AAAI

View full text Add to dashboard Cite

Most of the existing aspect-based sentiment analysis (ABSA) models only predict the sentiment polarity of a single aspect at a time, focusing primarily on enhancing the representation of this single aspect based on the other contexts or aspects. This one-to-one paradigm ignores the fact that multi-aspect, multi-sentiment sentences contain not only distinct specific descriptions for distinct specific aspects, but also shared global context information for multiple aspects. To fully consider these issues, we propose a one-to-many ABSA framework, called You Only Read Once (YORO), that can simultaneously model representations of all aspects based on their specific descriptions and better fuse their relationships using globally shared contextual information in the sentence. Predicting the sentiment polarity of multiple aspects simultaneously is beneficial to improving the efficacy of calculation and prediction. Extensive experiments are conducted on three public datasets (MAMS, Rest14, and Lap14). Experimental results demonstrate the effectiveness of YORO in handling multi-aspect, multi-sentiment scenarios and highlight the promise of one-to-many ABSA in balancing efficiency and accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification

Zheng,

2024

AAAI

View full text Add to dashboard Cite

show abstract

Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-Based Sentiment Analysis

Cited by 1 publication

References 41 publications

You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification

You Only Read Once: Constituency-Oriented Relational Graph Convolutional Network for Multi-Aspect Multi-Sentiment Classification

Contact Info

Product

Resources

About