A joint extraction model of entities and relations based on relation decomposition

Gao, Chen; Zhang, Xuan; Liu, Hui; Yun, Woo-Seop; Jiang, Jiahao

doi:10.1007/s13042-021-01491-6

Cited by 9 publications

(5 citation statements)

References 31 publications

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“… PA-LSTM 5 : this model uses a location-attentive long and short-term neural network, by which all entities and their types, as well as all overlapping relations, can be extracted simultaneously. GraphRel 26 : this model proposes an end-to-end relationship extraction model that uses a graph convolutional network for joint learning of entities and relationships, which helps in the prediction of overlapping relationships. NovelTagging 7 : this model proposes a new tagging scheme for simultaneous annotation of entities and their relations directly, instead of extracting entities and relations separately.…”

Section: Methodsmentioning

confidence: 99%

“…GraphRel 26 : this model proposes an end-to-end relationship extraction model that uses a graph convolutional network for joint learning of entities and relationships, which helps in the prediction of overlapping relationships.…”

Section: Methodsmentioning

confidence: 99%

“…Zheng et al 1 used parameter sharing to make entity identification and relationship extraction interact with each other to achieve the complementary advantages between tasks; Adel and Schütze 2 used a table-filling approach to fill the diagonal and non-diagonal lines of a table with entity labels and relationship labels, respectively, and model their relationships. Gao et al 3 divided extraction into relational extraction and entity recognition. Relationship extraction is transformed into a relationship classification task.…”

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confidence: 99%

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Joint extraction model of entity relations based on decomposition strategy

Li,

La,

Lei

et al. 2024

Sci Rep

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Named entity recognition and relation extraction are two important fundamental tasks in natural language processing. The joint entity-relationship extraction model based on parameter sharing can effectively reduce the impact of cascading errors on model performance by performing joint learning of entities and relationships in a single model, but it still cannot essentially get rid of the influence of pipeline models and suffers from entity information redundancy and inability to recognize overlapping entities. To this end, we propose a joint extraction model based on the decomposition strategy of pointer mechanism is proposed. The joint extraction task is divided into two parts. First, identify the head entity, utilizing the positive gain effect of the head entity on tail entity identification.Then, utilize a hierarchical model to improve the accuracy of the tail entity and relationship identification. Meanwhile, we introduce a pointer model to obtain the joint features of entity boundaries and relationship types to achieve boundary-aware classification. The experimental results show that the model achieves better results on both NYT and WebNLG datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Joint extraction model of entity relations based on decomposition strategy

Li,

La,

Lei

et al. 2024

Sci Rep

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“…Nevertheless, this extraction approach has certain shortcomings. Firstly, it faces the issue of error accumulation, where any errors in entity recognition will consequently impact the subsequent relationship classification results, leading to further error propagation [ 36 ]. Secondly, there is an underutilization of information, as the tasks of entity extraction and relationship extraction are relatively independent, and the inherent connection between the two is not effectively leveraged, particularly with relationship extraction failing to capitalize on the relationship between the two [ 37 ].…”

Section: Knowledge Graph Construction For Aircraft Health Managementmentioning

confidence: 99%

Fault Knowledge Graph Construction and Platform Development for Aircraft PHM

Meng,

Jing,

Wang

et al. 2023

Sensors

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To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.

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“…The two primary kinds of existing entity–relationship extraction methods are the pipeline extraction method and the joint model extraction method [ 7 ]. Among them, the pipeline extraction method divides the relationship extraction task into two independent subtasks, firstly, identifying the entities in the given text, and then identifying the relationship between entities [ 8 , 9 , 10 ]. The joint model extraction method recognizes the entities and the relationship between entities at the same time.…”

Section: Introductionmentioning

confidence: 99%

A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding

Tao

Sun

Zhang

2023

Entropy

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The entity–relationship joint extraction model plays a significant role in entity relationship extraction. The existing entity–relationship joint extraction model cannot effectively identify entity–relationship triples in overlapping relationships. This paper proposes a new joint entity–relationship extraction model based on the span and a cascaded dual decoding. The model includes a Bidirectional Encoder Representations from Transformers (BERT) encoding layer, a relational decoding layer, and an entity decoding layer. The model first converts the text input into the BERT pretrained language model into word vectors. Then, it divides the word vectors based on the span to form a span sequence and decodes the relationship between the span sequence to obtain the relationship type in the span sequence. Finally, the entity decoding layer fuses the span sequences and the relationship type obtained by relation decoding and uses a bi-directional long short-term memory (Bi-LSTM) neural network to obtain the head entity and tail entity in the span sequence. Using the combination of span division and cascaded double decoding, the overlapping relations existing in the text can be effectively identified. Experiments show that compared with other baseline models, the F1 value of the model is effectively improved on the NYT dataset and WebNLG dataset.

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A joint extraction model of entities and relations based on relation decomposition

Cited by 9 publications

References 31 publications

Joint extraction model of entity relations based on decomposition strategy

Joint extraction model of entity relations based on decomposition strategy

Fault Knowledge Graph Construction and Platform Development for Aircraft PHM

A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding

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