Papers Zero-shot Relation Triplet Extraction
“Zero-shot Relation Triplet Extraction” 태그가 달린 논문 5편 · 필터 해제
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction
The zero-shot relation triplet extraction (ZeroRTE) task aims to extract relation triplets from a piece of text with unseen relation types. The seminal work adopts the pre-trained generative model to generate synthetic s…
General KnowledgeMeta-LearningRelationTriplet+1Zero-shot Triplet Extraction by Template Infilling
The task of triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. Most existing methods train an extraction model on training data involving specific target relati…
Data AugmentationLanguage ModelingLanguage ModellingTriplet+2PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection
Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets from unstructured texts under the zero-shot setting, where the relation sets at the training and testing stages are disjoint. Previous sta…
Boundary DetectionRelationTripletZero-shot Relation Triplet ExtractionRelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task setting of Zero-Shot Relation Triplet Extrac…
Language ModelingLanguage ModellingRelationRelation Classification+5Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
Named entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task as a table-fi…
Joint Entity and Relation Extractionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4