Decoupling and Refilling: A Simple Data Augmentation Method for Aspect Term Extraction

Aspect term extraction (ATE) is an important Natural Language Processing task, which aims to extract aspect terms from reviews. Recently, data augmentation has emerged as a reliable approach for relieving data sparsity in the NLP area. For ATE, self-labeling and semi-generation methods have been pro...

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Bibliographic Details
Published inICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 12582 - 12586
Main Authors Chen, Jiaxiang, Hong, Yu, Liu, Chaoqun, Xu, Qingting, Zhou, Guodong
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
Aspect Term Extraction
Data augmentation
Data mining
Data models
Natural language processing
Reliability
Sequence Labeling
Tagging
Task analysis
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Summary:Aspect term extraction (ATE) is an important Natural Language Processing task, which aims to extract aspect terms from reviews. Recently, data augmentation has emerged as a reliable approach for relieving data sparsity in the NLP area. For ATE, self-labeling and semi-generation methods have been proposed to implement effective data augmentation. However, they either rely on external data or a pretrained generation model. In this paper, we propose a simple and self-contained augmentation method, which produces new instances for augmentation by context decoupling and infrequent term refilling, without using external data and generation models. We conduct experiments on four benchmark SemEval datasets. The test results show that our method yields substantial improvements, and performs comparably to the state-of-the-art method which uses external data.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10446120