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 …
S Guan, X Cheng, L Bai, F Zhang, Z Li… - … on Knowledge and …, 2022 - ieeexplore.ieee.org
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 …
D Lou, Z Liao, S Deng, N Zhang, H Chen - arXiv preprint arXiv:2105.09458, 2021 - arxiv.org
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 …