ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese
This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/11/8/1530 |
| _version_ | 1827686239098109952 |
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| author | Xiaomin Wang Haoriqin Wang Guocheng Zhao Zhichao Liu Huarui Wu |
| author_facet | Xiaomin Wang Haoriqin Wang Guocheng Zhao Zhichao Liu Huarui Wu |
| author_sort | Xiaomin Wang |
| collection | DOAJ |
| description | This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service. |
| first_indexed | 2024-03-10T09:05:32Z |
| format | Article |
| id | doaj.art-ad40947085774d72a3146ced3c0adcb8 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T09:05:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-ad40947085774d72a3146ced3c0adcb82023-11-22T06:25:10ZengMDPI AGAgronomy2073-43952021-07-01118153010.3390/agronomy11081530ALBERT over Match-LSTM Network for Intelligent Questions Classification in ChineseXiaomin Wang0Haoriqin Wang1Guocheng Zhao2Zhichao Liu3Huarui Wu4Beijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaThis paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service.https://www.mdpi.com/2073-4395/11/8/1530ALBERTmatch-LSTMnatural language processingclassificationNQuAD |
| spellingShingle | Xiaomin Wang Haoriqin Wang Guocheng Zhao Zhichao Liu Huarui Wu ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese Agronomy ALBERT match-LSTM natural language processing classification NQuAD |
| title | ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese |
| title_full | ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese |
| title_fullStr | ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese |
| title_full_unstemmed | ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese |
| title_short | ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese |
| title_sort | albert over match lstm network for intelligent questions classification in chinese |
| topic | ALBERT match-LSTM natural language processing classification NQuAD |
| url | https://www.mdpi.com/2073-4395/11/8/1530 |
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