Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that
identifies aspects and their associated opinions from a given text. With the surge of digital …

The data-hungry problem, characterized by insufficiency and low-quality of data, poses
obstacles for deep learning models. Transfer learning has been a feasible way to transfer …

Aspect-based Sentiment Analysis (ABSA) is a type of fine-grained sentiment analysis (SA)
that identifies aspects and the associated opinions from a given text. In the digital era, ABSA …

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity
pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges …

Abstract Aspect-based Sentiment Analysis (ABSA) is extensively researched in the NLP
community, yet related models face challenges due to data sparsity when shifting to a new …

Aspect term extraction is a fundamental task in fine-grained sentiment analysis, which aims
at detecting customer's opinion targets from reviews on product or service. The traditional …

Existing knowledge graph (KG) models for commonsense question answering present two
challenges:(i) existing methods retrieve entities related to questions from the knowledge …

Aspect term extraction is a fundamental task in fine-grained sentiment analysis, aiming to
detect customer's opinion targets from reviews about products or services. The traditional …

Aspect-level sentiment analysis is crucial for consumers and institutions, enabling them to
monitor satisfaction regarding specific aspects of products and services through user …

Cross-domain relation extraction, an emerging research task, evaluates the generalization
ability of relation extraction models across different domains. However, existing studies lack …