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

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 …

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 …

The unprecedented performance of large language models (LLMs) necessitates
improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we …

To accelerate biomedical research process, deep-learning systems are developed to
automatically acquire knowledge about molecule entities by reading large-scale biomedical …

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 …

In this paper, a semantic communication framework is proposed for textual data
transmission. In the studied model, a base station (BS) extracts the semantic information …

Document-level relation extraction aims to extract relations among entities within a
document. Different from sentence-level relation extraction, it requires reasoning over …

Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …

Document-level relation extraction aims to extract relations among multiple entity pairs from
a document. Previously proposed graph-based or transformer-based models utilize the …