Abstract Information systems need to process a large amount of event monitoring data. The
process of finding the relationships between events is called correlation, which creates a …

Self-supervised protein language models have proved their effectiveness in learning the
proteins representations. With the increasing computational power, current protein language …

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

We consider the problem of collectively detecting multiple events, particularly in cross-
sentence settings. The key to dealing with the problem is to encode semantic information …

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in
semantic search. Prior conceptual graph construction approaches typically extract high …

We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in the …

Event detection (ED) aims at detecting event trigger words in sentences and classifying them
into specific event types. In real-world applications, ED typically does not have sufficient …

Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes
two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we …

Information extraction tasks such as entity relation extraction and event extraction are of
great importance for natural language processing and knowledge graph construction. In this …