Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network
With the development of Internet technology, government affairs can be handled online. More and more citizens are using online platforms to report to government departments, which is generating a lot of textual data. Among them, the basic but important problem is to automatically classify the differ...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/6540696 |
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| _version_ | 1832554755183869952 |
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| author | Mingxin Li Kaiqian Yin Minghao Wang |
| author_facet | Mingxin Li Kaiqian Yin Minghao Wang |
| author_sort | Mingxin Li |
| collection | DOAJ |
| description | With the development of Internet technology, government affairs can be handled online. More and more citizens are using online platforms to report to government departments, which is generating a lot of textual data. Among them, the basic but important problem is to automatically classify the different categories of messages, so that staff from different departments can process relevant information quickly. However, government messages have problems such as fast update rate, a large amount of information, long texts, and difficulty in capturing key points, which make supervised learning methods unsuitable for processing such texts. To address these problems, we propose a semisupervised text classification method based on a transformer-based pointer generator network named Ptr4BERT, which uses the pointer generator network with BERT(bidirectional encoder representation from transformers) embedding as a preprocessor for feature extraction. In this method, text classification can achieve very good results with a small set of labeled data, by extracting features exclusively from the message text. In order to verify the effect of our proposed model, we performed some experiments. Besides, we designed a crawler program and obtained two datasets from different websites, which are named HNMes and QDMes. Experimental results have shown that the proposed method outperforms the state-of-the-art methods significantly. |
| format | Article |
| id | doaj-art-4c2584ca531e452e811102fb2775ef57 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-4c2584ca531e452e811102fb2775ef572025-02-03T05:50:38ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/6540696Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator NetworkMingxin Li0Kaiqian Yin1Minghao Wang2College of Computer Science and EngineeringCollege of Mathematics and Systems ScienceCollege of Mechanical and Electronic EngineeringWith the development of Internet technology, government affairs can be handled online. More and more citizens are using online platforms to report to government departments, which is generating a lot of textual data. Among them, the basic but important problem is to automatically classify the different categories of messages, so that staff from different departments can process relevant information quickly. However, government messages have problems such as fast update rate, a large amount of information, long texts, and difficulty in capturing key points, which make supervised learning methods unsuitable for processing such texts. To address these problems, we propose a semisupervised text classification method based on a transformer-based pointer generator network named Ptr4BERT, which uses the pointer generator network with BERT(bidirectional encoder representation from transformers) embedding as a preprocessor for feature extraction. In this method, text classification can achieve very good results with a small set of labeled data, by extracting features exclusively from the message text. In order to verify the effect of our proposed model, we performed some experiments. Besides, we designed a crawler program and obtained two datasets from different websites, which are named HNMes and QDMes. Experimental results have shown that the proposed method outperforms the state-of-the-art methods significantly.http://dx.doi.org/10.1155/2022/6540696 |
| spellingShingle | Mingxin Li Kaiqian Yin Minghao Wang Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network Advances in Multimedia |
| title | Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network |
| title_full | Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network |
| title_fullStr | Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network |
| title_full_unstemmed | Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network |
| title_short | Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network |
| title_sort | ptr4bert automatic semisupervised chinese government message text classification method based on transformer based pointer generator network |
| url | http://dx.doi.org/10.1155/2022/6540696 |
| work_keys_str_mv | AT mingxinli ptr4bertautomaticsemisupervisedchinesegovernmentmessagetextclassificationmethodbasedontransformerbasedpointergeneratornetwork AT kaiqianyin ptr4bertautomaticsemisupervisedchinesegovernmentmessagetextclassificationmethodbasedontransformerbasedpointergeneratornetwork AT minghaowang ptr4bertautomaticsemisupervisedchinesegovernmentmessagetextclassificationmethodbasedontransformerbasedpointergeneratornetwork |