Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis
(ABSA), aiming to analyze and understand people's opinions at the aspect level, has been …

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither)
towards a target in given texts. Such research gains increasing attention with the …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Business failure prediction or survival analysis can assist corporate organizations in better
understanding their performance and improving decision making. Based on aspect-based …

Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to
detection the sentiment polarity towards a given aspect. Recently, graph neural models over …

Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific
aspect in online reviews. Most recent efforts adopt attention-based neural network models to …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

The state-of-the-art methods in aspect-level sentiment classification have leveraged the
graph based models to incorporate the syntactic structure of a sentence. While being …