A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model
To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of se...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6610965 |
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| _version_ | 1832559757136756736 |
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| author | Chengyao Lv Deng Pan Yaxiong Li Jianxin Li Zong Wang |
| author_facet | Chengyao Lv Deng Pan Yaxiong Li Jianxin Li Zong Wang |
| author_sort | Chengyao Lv |
| collection | DOAJ |
| description | To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models. |
| format | Article |
| id | doaj-art-3d3caa36211d446faff105a414e139cd |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3d3caa36211d446faff105a414e139cd2025-02-03T01:29:18ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66109656610965A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov ModelChengyao Lv0Deng Pan1Yaxiong Li2Jianxin Li3Zong Wang4School of Foreign Languages, China University of Geosciences, Wuhan 430074, ChinaSchool of Foreign Languages, Hubei University of Science and Technology, Xianning 437000, ChinaInformation Centre, Hubei University of Science and Technology, Xianning 437000, ChinaSchool of Information Technology, Deakin University, Geelong VIC3220, AustraliaSchool of Public Administration, China University of Geosciences, Wuhan 430074, ChinaTo identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.http://dx.doi.org/10.1155/2021/6610965 |
| spellingShingle | Chengyao Lv Deng Pan Yaxiong Li Jianxin Li Zong Wang A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model Complexity |
| title | A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model |
| title_full | A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model |
| title_fullStr | A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model |
| title_full_unstemmed | A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model |
| title_short | A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model |
| title_sort | novel chinese entity relationship extraction method based on the bidirectional maximum entropy markov model |
| url | http://dx.doi.org/10.1155/2021/6610965 |
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