Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

Neural models have achieved remarkable success on relation extraction (RE) benchmarks.
However, there is no clear understanding which type of information affects existing RE …

Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data
even if under zero-shot setting. Recent studies have shown that large language models …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Multimodal relation extraction is a critical task in information extraction, aiming to predict the
class of relations between head and tail entities from linguistic sequences and related …

Ensuring factual accuracy while maintaining the creative capabilities of Large Language
Model Agents (LMAs) poses significant challenges in the development of intelligent agent …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …