Aspect Term Extraction

Aspect term extraction (ATE) aims to identify specific aspects or features within text, such as product attributes in reviews, upon which opinions are expressed. Current research focuses on improving ATE accuracy using advanced deep learning models, particularly transformer-based architectures like BERT and its variants, often incorporating techniques like hybrid models, contrastive learning, and prompt tuning to enhance performance and address challenges like implicit aspects and cross-domain adaptation. The ability to accurately extract aspects is crucial for various applications, including sentiment analysis, opinion mining, and improving user experience in areas like e-commerce and product development. This field is actively developing new datasets and frameworks to facilitate reproducible research and advance the state-of-the-art.