A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/8/1217 |
| _version_ | 1797584861242851328 |
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| author | Tao Liao Haojie Sun Shunxiang Zhang |
| author_facet | Tao Liao Haojie Sun Shunxiang Zhang |
| author_sort | Tao Liao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:57:44Z |
| format | Article |
| id | doaj.art-dad77efcbe3141da85cd852b87437f0a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T23:57:44Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-dad77efcbe3141da85cd852b87437f0a2023-11-19T01:00:12ZengMDPI AGEntropy1099-43002023-08-01258121710.3390/e25081217A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual DecodingTao Liao0Haojie Sun1Shunxiang Zhang2College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaCollege of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaCollege of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaThe 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.https://www.mdpi.com/1099-4300/25/8/1217entity relation extractionspandecodecascadeneural network |
| spellingShingle | Tao Liao Haojie Sun Shunxiang Zhang A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding Entropy entity relation extraction span decode cascade neural network |
| title | A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding |
| title_full | A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding |
| title_fullStr | A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding |
| title_full_unstemmed | A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding |
| title_short | A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding |
| title_sort | joint extraction model for entity relationships based on span and cascaded dual decoding |
| topic | entity relation extraction span decode cascade neural network |
| url | https://www.mdpi.com/1099-4300/25/8/1217 |
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