A comprehensive survey on relation extraction: Recent advances and new frontiers
Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …
content. RE serves as the foundation for many natural language processing (NLP) and …
Generative knowledge graph construction: A review
Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …
Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction
Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …
Differentiable prompt makes pre-trained language models better few-shot learners
Large-scale pre-trained language models have contributed significantly to natural language
processing by demonstrating remarkable abilities as few-shot learners. However, their …
processing by demonstrating remarkable abilities as few-shot learners. However, their …
Document-level relation extraction as semantic segmentation
Document-level relation extraction aims to extract relations among multiple entity pairs from
a document. Previously proposed graph-based or transformer-based models utilize the …
a document. Previously proposed graph-based or transformer-based models utilize the …
Ontoprotein: Protein pretraining with gene ontology embedding
Self-supervised protein language models have proved their effectiveness in learning the
proteins representations. With the increasing computational power, current protein language …
proteins representations. With the increasing computational power, current protein language …
Ontology-enhanced Prompt-tuning for Few-shot Learning
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 …
samples. Structured data such as knowledge graphs and ontology libraries has been …
The joint method of triple attention and novel loss function for entity relation extraction in small data-driven computational social systems
With the development of the social Internet of Things (IoT) and multimedia communications,
our daily lives in computational social systems have become more convenient; for example …
our daily lives in computational social systems have become more convenient; for example …
Defending pre-trained language models as few-shot learners against backdoor attacks
Pre-trained language models (PLMs) have demonstrated remarkable performance as few-
shot learners. However, their security risks under such settings are largely unexplored. In …
shot learners. However, their security risks under such settings are largely unexplored. In …
Deepke: A deep learning based knowledge extraction toolkit for knowledge base population
We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in the …
supporting complicated low-resource, document-level and multimodal scenarios in the …