3 accepted papers
Document-level relation extraction (DocRE) aims to extract structured relational triples from unstructured text based on given entities. Existing methods are mainly categorized into transformer-based models and graph-based models. While transformer-based models capture global contextual information,…
In this paper, we propose a novel framework that leverages Large Language Models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors an…
Document-level Relation Extraction (DocRE) aims to extract relations between entity pairs in a document and poses many challenges as it involves multiple mentions of entities and cross-sentence inference. However, several aspects that are important for DocRE have not been considered and explored. Ex…