Joint Entity and Relation Extraction
53 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
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
pgcool/TF-MTRNN
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COLING 2016
This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.
Adversarial training for multi-context joint entity and relation extraction
bekou/multihead_joint_entity_relation_extraction
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EMNLP 2018
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data.
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
tsujuifu/pytorch_graph-rel
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ACL 2019
In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better extract relations.
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
genggui001/fine_tuning_bert_for_joint_entity_and_relation_extraction_in_chinese_medical_text
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Entity and relation extraction is the necessary step in structuring medical text.
Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
nusnlp/PtrNetDecoding4JERE
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A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text.
Deeper Task-Specificity Improves Joint Entity and Relation Extraction
vedantc6/mtl-dts
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Multi-task learning (MTL) is an effective method for learning related tasks, but designing MTL models necessitates deciding which and how many parameters should be task-specific, as opposed to shared between tasks.
A Relation-Specific Attention Network for Joint Entity and Relation Extraction
Anery/RSAN
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Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts.
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
WindChimeRan/OpenJERE
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Findings of the Association for Computational Linguistics 2020
We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
Joint Entity and Relation Extraction with Set Prediction Networks
Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities.
