A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms
The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and util...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1058 |
| _version_ | 1827354948256399360 |
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| author | Ping Feng Nannan Su Jiamian Xing Jing Bian Dantong Ouyang |
| author_facet | Ping Feng Nannan Su Jiamian Xing Jing Bian Dantong Ouyang |
| author_sort | Ping Feng |
| collection | DOAJ |
| description | The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results. |
| first_indexed | 2024-03-08T04:01:03Z |
| format | Article |
| id | doaj.art-e2a93568d5dd421d8734922e2beb776c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:01:03Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e2a93568d5dd421d8734922e2beb776c2024-02-09T15:07:32ZengMDPI AGApplied Sciences2076-34172024-01-01143105810.3390/app14031058A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference AlgorithmsPing Feng0Nannan Su1Jiamian Xing2Jing Bian3Dantong Ouyang4College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Cybersecurity, Changchun University, Changchun 130022, ChinaCollege of Cybersecurity, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaThe purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results.https://www.mdpi.com/2076-3417/14/3/1058named entity recognitionrelationship extractiondatamodelinference algorithm |
| spellingShingle | Ping Feng Nannan Su Jiamian Xing Jing Bian Dantong Ouyang A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms Applied Sciences named entity recognition relationship extraction data model inference algorithm |
| title | A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms |
| title_full | A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms |
| title_fullStr | A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms |
| title_full_unstemmed | A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms |
| title_short | A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms |
| title_sort | study of entity relationship extraction algorithms based on symmetric interaction between data models and inference algorithms |
| topic | named entity recognition relationship extraction data model inference algorithm |
| url | https://www.mdpi.com/2076-3417/14/3/1058 |
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