Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have
recently been successfully applied to tasks such as information retrieval, question …

Multimodal sentiment analysis aims to extract and integrate semantic information collected
from multiple modalities to recognize the expressed emotions and sentiment in multimodal …

Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Attention is an increasingly popular mechanism used in a wide range of neural
architectures. The mechanism itself has been realized in a variety of formats. However …

As an emerging task in opinion mining, End-to-End Multimodal Aspect-Based Sentiment
Analysis (MABSA) aims to extract all the aspect-sentiment pairs mentioned in a pair of …

Multimodal named entity recognition and relation extraction (MNER and MRE) is a
fundamental and crucial branch in information extraction. However, existing approaches for …

Multi-modal named entity recognition (MNER) aims to discover named entities in free text
and classify them into pre-defined types with images. However, dominant MNER models do …