Joint Entity and Relation Extraction With Set Prediction Networks

Sui, Dianbo; Zeng, Xiangrong; Chen, Yubo; Liu, Kang; Zhao, Jun

doi:10.1109/tnnls.2023.3264735

Cited by 47 publications

(9 citation statements)

References 48 publications

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“…To verify the performance of BCSLinker, we have the following four advanced baseline models involved in the comparison: SPN ( 21 ), CasRel ( 17 ), BiRTE ( 19 ), PRGC ( 18 ), OneRel ( 22 ), MultiHead ( 24 ), and GRTE ( 23 ).…”

Section: Resultsmentioning

confidence: 99%

“…Multi-module one-step extraction methods address error propagation in joint models by extracting entities and relations at one time and combining them into triples. SPN ( 21 ) transforms joint entity and relation extraction into a set prediction problem and combines non-autoregressive parallel decoding with a bipartite matching loss to address the relational triples prediction issue. Shang et al ( 22 ) presented OneRel, employing a score-based classifier to assess whether a token pair and a relation constitute a relational triple.…”

Section: Introductionmentioning

confidence: 99%

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BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning

Cai,

He,

Liu

et al. 2024

Front. Med.

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BackgroundVenous thromboembolism (VTE) is characterized by high morbidity, mortality, and complex treatment. A VTE knowledge graph (VTEKG) can effectively integrate VTE-related medical knowledge and offer an intuitive description and analysis of the relations between medical entities. However, current methods for constructing knowledge graphs typically suffer from error propagation and redundant information.MethodsIn this study, we propose a deep learning-based joint extraction model, Biaffine Common-Sequence Self-Attention Linker (BCSLinker), for Chinese electronic medical records to address the issues mentioned above, which often occur when constructing a VTEKG. First, the Biaffine Common-Sequence Self-Attention (BCsSa) module is employed to create global matrices and extract entities and relations simultaneously, mitigating error propagation. Second, the multi-label cross-entropy loss is utilized to diminish the impact of redundant information and enhance information extraction.ResultsWe used the electronic medical record data of VTE patients from a tertiary hospital, achieving an F1 score of 86.9% on BCSLinker. It outperforms the other joint entity and relation extraction models discussed in this study. In addition, we developed a question-answering system based on the VTEKG as a structured data source.ConclusionThis study has constructed a more accurate and comprehensive VTEKG that can provide reference for diagnosing, evaluating, and treating VTE as well as supporting patient self-care, which is of considerable clinical value.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning

Cai,

He,

Liu

et al. 2024

Front. Med.

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“…Wei et al [23] introduced a Casrel model that addresses the issue of multiple entity relationships and overlapping relationship triplets within a single sentence by establishing a Hierarchical Binary Tagging framework. Another common approach is the multi-module single-step modeling method [24][25][26] , which addresses the issue of insufficient interaction of entity relationship information by establishing a joint decoder. One typical model is TPLinker [27], which considers entity relation joint extraction as a token pair linking problem.…”

Section: Current Methods Of Information Extractionmentioning

confidence: 99%

RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction

Ye,

Qi,

Liu

et al. 2024

Energies

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The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model’s extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model’s universal applicability.

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“…This joint optimization aims to harness the interplay between the two tasks, thereby enhancing overall performance Zhao et al 2021;Ye et al 2022a). Noteworthy directions in this domain include table-filling methods (Wang and Lu 2020;Ma, Hiraoka, and Okazaki 2022), span pair classification (Eberts and Ulges 2019;Wadden et al 2019), set prediction (Sui et al 2020), augmented sequence tagging mechanisms (Ji et al 2020), fine-grained triplet classification (Shang, Huang, and Mao 2022), and the use of unified labels for the task (Wang et al 2021).…”

Section: Related Work Classification-based Iementioning

confidence: 99%

An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction

Zaratiana,

Tomeh,

Holat

et al. 2024

AAAI

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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. In contrast to conventional generative information extraction models that are left-to-right token-level generators, our approach is \textit{span-based}. It generates a linearized graph where nodes represent text spans and edges represent relation triplets. Our method employs a transformer encoder-decoder architecture with pointing mechanism on a dynamic vocabulary of spans and relation types. Our model can capture the structural characteristics and boundaries of entities and relations through span representations while simultaneously grounding the generated output in the original text thanks to the pointing mechanism. Evaluation on benchmark datasets validates the effectiveness of our approach, demonstrating competitive results. Code is available at https://github.com/urchade/ATG.

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Joint Entity and Relation Extraction With Set Prediction Networks

Cited by 47 publications

References 48 publications

BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning

BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning

RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction

An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction

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