Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …

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

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

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

Document-level Relation Extraction (DocRE) is a more challenging task compared to its
sentence-level counterpart. It aims to extract relations from multiple sentences at once. In …

Relation Extraction (RE) is a fundamental task of information extraction, which has attracted
a large amount of research attention. Previous studies focus on extracting the relations …

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

Recently, numerous efforts have continued to push up performance boundaries of document-
level relation extraction (DocRE) and have claimed significant progress in DocRE. In this …