Abstract Document-level Relation Extraction (RE) aims to infer complex semantic relations
between entities in a document. Previous approaches leverage a multi-classification model …

Few-shot knowledge graph completion focuses on predicting unseen facts of long-tail
relations in knowledge graphs with only few reference sets. The key challenge for tackling …

Attributed graph clustering is an essential research topic on real-world data. However, the
heterogeneous gap between node and structure features limits the existing approaches for …

Targeted sentiment analysis aims to identify the sentiment polarity of specific target mentions
in a sentence. Existing methods employ neural networks to extract the relations between …

Document-level relation extraction (DocRE) is an active area of research in natural
language processing (NLP) concerned with identifying and extracting relationships between …

The increasing number of cyberattacks has made the cybersecurity situation more serious.
Thus, it is urgent to use cyber threat intelligence to deal with the complex and changing …

Crowdfunding creates opportunities for creative people to raise funds so their ideas can be
brought to life. However, the failure of fundraising leads to certain losses for project starters …

Relation extraction, a fundamental task in natural language processing, aims to extract entity
triples from unstructured data. These triples can then be used to build a knowledge graph …

Graph convolutional network (GCN) algorithms have been employed to learn graph
embedding due to its inductive inference property, which is extended to GCN with higher …

Abstract Contextual Path Generation (CPG) refers to the task of generating knowledge path
(s) between a pair of entities mentioned in an input textual context to determine the semantic …