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
Latest papers
An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
urchade/atg
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In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem.
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
yanzhh/hgere
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Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial. In this work, we propose HyperGraph neural network for ERE ($\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model).
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
yul091/denrl
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However, existing research primarily addresses only one type of noise, thereby limiting the effectiveness of noise reduction.
CARE: Co-Attention Network for Joint Entity and Relation Extraction
However, most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between the two subtasks.
Similarity-based Memory Enhanced Joint Entity and Relation Extraction
kosciukiewicz/similarity_based_memory_re
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Document-level joint entity and relation extraction is a challenging information extraction problem that requires a unified approach where a single neural network performs four sub-tasks: mention detection, coreference resolution, entity classification, and relation extraction.
End-to-End Temporal Relation Extraction in the Clinical Domain
jsaizant/ETEREX-REBEL
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Proceedings of the Text2Story'23 Workshop 2023
Temporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events.
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
Knowledge Graph Generation From Text
ibm/grapher
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In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages.
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes.
SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks
Eunhui-Kim/SciDeBERTa-Fine-Tuning
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IEEE Access 2022
Experiments verified that SciDeBERTa(CS) continually pre-trained in the computer science domain achieved 3. 53% and 2. 17% higher accuracies than SciBERT and S2ORC-SciBERT, respectively, which are science technology domain specialized PLMs, in the task of recognizing entity names in SciERC dataset.
