Few-shot document-level relation extraction suffers from poor performance due to the
challenging cross-domain transferability of NOTA (none-of-the-above) relation …

Despite widespread applications of knowledge graphs (KGs) in various tasks such as
question answering and intelligent conversational systems, existing KGs face two major …

Differentiating relationships between entity pairs with limited labeled instances poses a
significant challenge in few-shot relation classification. Representations of textual data …

Downstream applications often require text classification models to be accurate, robust, and
interpretable. While the accuracy of the stateof-the-art language models approximates …

This study introduces a novel approach to sentence-level relation extraction (RE) that
integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate …

Abstract Language technologies are widely spreading over a diverse range of applications.
Therefore, the ability of computational systems to easily adapt to new unseen situations is …

Few-shot relation extraction (FSRE) is a challenging task in natural language processing. It
requires models to accurately identify and categorize the relation between entities in texts …

Like never before and well beyond the imagination of most of us, artificial intelligence (AI)
software is increasingly becoming commonplace. Seemingly overnight, AI models that …