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
Latest papers with no code
EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs.
Enhancing Knowledge Graph Construction Using Large Language Models
The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently.
90% F1 Score in Relational Triple Extraction: Is it Real ?
This approach leads to overall performance improvement in these models within the realistic experimental setting.
Query-based Instance Discrimination Network for Relational Triple Extraction
Joint entity and relation extraction has been a core task in the field of information extraction.
Span-based joint entity and relation extraction augmented with sequence tagging mechanism
On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE.
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes
To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain.
A Two-Phase Paradigm for Joint Entity-Relation Extraction
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
Our generative model is a unified framework to sequentially generate relational triplets under various relation extraction settings and explicitly utilizes relevant knowledge from Knowledge Graph (KG) to resolve ambiguities.
STable: Table Generation Framework for Encoder-Decoder Models
The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks.
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction
We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets evaluated in an entity-centric way.
