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

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

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in
modeling graph-structured data. However, the primary focus has been on handling simple …

Most existing knowledge graphs suffer from incompleteness, which can be alleviated by
inferring missing links based on known facts. One popular way to accomplish this is to …

Abstract Graph Neural Networks (GNN) have been shown to work effectively for modeling
graph structured data to solve tasks such as node classification, link prediction and graph …

Document-level relation extraction is a complex human process that requires logical
inference to extract relationships between named entities in text. Existing approaches use …

A relation tuple consists of two entities and the relation between them, and often such tuples
are found in unstructured text. There may be multiple relation tuples present in a text and …

Distantly supervised relation extraction is widely used to extract relational facts from text, but
suffers from noisy labels. Current relation extraction methods try to alleviate the noise by …

Active learning methods which present selected examples from the corpus for annotation
provide more efficient learning of supervised relation extraction models, but they leave the …