Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network
Keyn-gram extraction can be seen as extracting n-grams which can distinguish different registers. Keyword (as n=1, 1-gram is the keyword) extraction models are generally carried out from two aspects, the feature extraction and the model design. By summarizing the advantages and disadvantages of exis...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/5264090 |
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| _version_ | 1832550569270575104 |
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| author | Haiyan Wu Ying Liu Shaoyun Shi Qingfeng Wu Yunlong Huang |
| author_facet | Haiyan Wu Ying Liu Shaoyun Shi Qingfeng Wu Yunlong Huang |
| author_sort | Haiyan Wu |
| collection | DOAJ |
| description | Keyn-gram extraction can be seen as extracting n-grams which can distinguish different registers. Keyword (as n=1, 1-gram is the keyword) extraction models are generally carried out from two aspects, the feature extraction and the model design. By summarizing the advantages and disadvantages of existing models, we propose a novel key n-gram extraction model “attentive n-gram network” (ANN) based on the attention mechanism and multilayer perceptron, in which the attention mechanism scores each n-gram in a sentence by mining the internal semantic relationship between words, and their importance is given by the scores. Experimental results on the real corpus show that the key n-gram extracted from our model can distinguish a novel, news, and text book very well; the accuracy of our model is significantly higher than the baseline model. Also, we conduct experiments on key n-grams extracted from these registers, which turned out to be well clustered. Furthermore, we make some statistical analyses of the results of key n-gram extraction. We find that the key n-grams extracted by our model are very explanatory in linguistics. |
| format | Article |
| id | doaj-art-8539fa294b934bf9a34cca43098e1048 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-8539fa294b934bf9a34cca43098e10482025-02-03T06:06:27ZengWileyJournal of Applied Mathematics1110-757X1687-00422021-01-01202110.1155/2021/52640905264090Key n-Gram Extractions and Analyses of Different Registers Based on Attention NetworkHaiyan Wu0Ying Liu1Shaoyun Shi2Qingfeng Wu3Yunlong Huang4Zhejiang University of Finance and Economics, Hangzhou 310018, ChinaSchool of Humanities, Tsinghua University, Beijing 100084, ChinaDepartment of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, ChinaChengdu Polytechnic, Chengdu 610095, ChinaBeijing Normal University, Beijing 100875, ChinaKeyn-gram extraction can be seen as extracting n-grams which can distinguish different registers. Keyword (as n=1, 1-gram is the keyword) extraction models are generally carried out from two aspects, the feature extraction and the model design. By summarizing the advantages and disadvantages of existing models, we propose a novel key n-gram extraction model “attentive n-gram network” (ANN) based on the attention mechanism and multilayer perceptron, in which the attention mechanism scores each n-gram in a sentence by mining the internal semantic relationship between words, and their importance is given by the scores. Experimental results on the real corpus show that the key n-gram extracted from our model can distinguish a novel, news, and text book very well; the accuracy of our model is significantly higher than the baseline model. Also, we conduct experiments on key n-grams extracted from these registers, which turned out to be well clustered. Furthermore, we make some statistical analyses of the results of key n-gram extraction. We find that the key n-grams extracted by our model are very explanatory in linguistics.http://dx.doi.org/10.1155/2021/5264090 |
| spellingShingle | Haiyan Wu Ying Liu Shaoyun Shi Qingfeng Wu Yunlong Huang Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network Journal of Applied Mathematics |
| title | Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network |
| title_full | Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network |
| title_fullStr | Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network |
| title_full_unstemmed | Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network |
| title_short | Key n-Gram Extractions and Analyses of Different Registers Based on Attention Network |
| title_sort | key n gram extractions and analyses of different registers based on attention network |
| url | http://dx.doi.org/10.1155/2021/5264090 |
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