A survey on open Information Extraction from rule-based model to large language model
Abstract Open Information Extraction (OpenIE) represents a crucial NLP task aimed at
deriving structured information from unstructured text, unrestricted by relation type or …
deriving structured information from unstructured text, unrestricted by relation type or …
[PDF][PDF] Open information extraction from 2007 to 2022–a survey
Open information extraction is an important NLP task that targets extracting structured
information from unstructured text without limitations on the relation type or the domain of the …
information from unstructured text without limitations on the relation type or the domain of the …
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models
Current clustering-based open relation extraction (OpenRE) methods usually apply
clustering algorithms on top of pre-trained language models. However, this practice has …
clustering algorithms on top of pre-trained language models. However, this practice has …
Topic-Oriented Open Relation Extraction with A Priori Seed Generation
The field of open relation extraction (ORE) has recently observed significant advancement
thanks to the growing capability of large language models (LLMs). Nevertheless, challenges …
thanks to the growing capability of large language models (LLMs). Nevertheless, challenges …
HRCL: Hierarchical Relation Contrastive Learning for Low-Resource Relation Extraction
Q Guo, Y Guo, J Zhao - IEEE Transactions on Neural Networks …, 2024 - ieeexplore.ieee.org
Low-resource relation extraction (LRE) aims to extract the relationships between given
entities from natural language sentences in low-resource application scenarios, which has …
entities from natural language sentences in low-resource application scenarios, which has …
ParTRE: A relational triple extraction model of complicated entities and imbalanced relations in Parkinson's disease
X Zhang, C Yu, R Yan - Journal of Biomedical Informatics, 2024 - Elsevier
The relational triple extraction of unstructured medical texts about Parkinson's disease is
critical for the construction of a medical knowledge graph. However, the triple entities in …
critical for the construction of a medical knowledge graph. However, the triple entities in …
The State of Relation Extraction Data Quality: Is Bigger Always Better?
E Cai, B O'Connor - Findings of the Association for Computational …, 2024 - aclanthology.org
Relation extraction (RE) extracts structured tuples of relationships (eg friend, enemy)
between entities (eg Sherlock Holmes, John Watson) from text, with exciting potential …
between entities (eg Sherlock Holmes, John Watson) from text, with exciting potential …
Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery
We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world
relation extraction. GRD aims to identify unlabeled instances in existing pre-defined …
relation extraction. GRD aims to identify unlabeled instances in existing pre-defined …