Equipping 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 …

As an essential component of human cognition, cause–effect relations appear frequently in
text, and curating cause–effect relations from text helps in building causal networks for …

Contextual word representations, typically trained on unstructured, unlabeled text, do not
contain any explicit grounding to real world entities and are often unable to remember facts …

We introduce SpERT, an attention model for span-based joint entity and relation extraction.
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …

Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …

Document-level relation extraction (RE) poses new challenges compared to its sentence-
level counterpart. One document commonly contains multiple entity pairs, and one entity pair …

Abstract Entities, as the essential elements in relation extraction tasks, exhibit certain
structure. In this work, we formulate such entity structure as distinctive dependencies …

Modelling relations between multiple entities has attracted increasing attention recently, and
a new dataset called DocRED has been collected in order to accelerate the research on the …

Classic methods for clinical temporal relation extraction focus on relational candidates within
a sentence. On the other hand, break-through Bidirectional Encoder Representations from …

Rapid advancements in language models (LMs) have led to their adoption across many
sectors. Alongside the potential benefits, such models present a range of risks, including …