Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets.
Keywords
Knowledge Graph, Multiview Learning, Feature Fusion, Aspect-Based Sentiment Analysis
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
2023 International Conference on Data Mining, ICDM: Shanghai, December 1-4: Proceedings
First Page
791
Last Page
798
ISBN
9798350381641
Identifier
10.1109/ICDMW60847.2023.00107
Publisher
IEEE Computer Society
City or Country
Washington, DC
Citation
TEO, Autumn; WANG, Zhaoxia; PEN, Haibo; SUBAGDJA, Budhitama; HO, Seng-Beng; and QUEK, Boon Kiat.
Knowledge graph enhanced aspect-based sentiment analysis incorporating external knowledge. (2023). 2023 International Conference on Data Mining, ICDM: Shanghai, December 1-4: Proceedings. 791-798.
Available at: https://ink.library.smu.edu.sg/sis_research/8408
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDMW60847.2023.00107
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons