Joint Entity and Relation Extraction
55 papers with code • 16 benchmarks • 16 datasets
Joint Entity and Relation Extraction is the task of extracting entity mentions and semantic relations between entities from unstructured text with a single model.
Benchmarks
These leaderboards are used to track progress in Joint Entity and Relation Extraction
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Datasets
New York Times Annotated Corpus
CoNLL
DocRED
WebNLG
SciERC
ACE 2005
CDR
Most implemented papers
Structured Prediction as Translation between Augmented Natural Languages
amazon-research/tanl
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ICLR 2021
We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
tricktreat/trimf
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Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model.
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
We present a joint model for entity-level relation extraction from documents.
Multilingual Entity and Relation Extraction Dataset and Model
We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction.
Deep Neural Networks for Relation Extraction
nusnlp/PtrNetDecoding4JERE
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Relation extraction from text is an important task for automatic knowledge base population.
Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
zhao9797/RIFRE
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Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text.
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
laituan245/bio_relex
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ACL 2021
It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text.
Effective Cascade Dual-Decoder Model for Joint Entity and Relation Extraction
The popular way of existing methods is to jointly extract entities and relations using a single model, which often suffers from the overlapping triple problem.
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
renll/HySPA
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Findings (ACL) 2021
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts.
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution
klimzaporojets/e2e-kb-ie
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Findings (ACL) 2021
The used KB entity representations are learned from either (i) hyperlinked text documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear complementary in raising IE performance.
