Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms
Online network education offers convenience, however, the inefficiency and time-consuming nature of question-answering models negatively impact the demand for online learning. To address this issue, the study puts forward the development of an automatic English question-answering model. The improved...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000164 |
| _version_ | 1797262819484237824 |
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| author | Hainan Wang |
| author_facet | Hainan Wang |
| author_sort | Hainan Wang |
| collection | DOAJ |
| description | Online network education offers convenience, however, the inefficiency and time-consuming nature of question-answering models negatively impact the demand for online learning. To address this issue, the study puts forward the development of an automatic English question-answering model. The improved model leverages a term frequence-inverse document frequency approach and an unsupervised participle algorithm based on deep learning. The precision and promptness of the question-answering model were enhanced by refining the weighted allocation of the term frequence-inverse document frequency algorithm and the unsupervised word-splitting algorithm. The validation shows that the improved precision rate is 68.14%, which is 34.37% and 50.45% more than the other two methods, respectively. The precision rate, recall rate, and F1 value for semantic similarity calculation improved by 9.23%, 9.22%, and 9.71%, respectively, compared to the traditional method. The validation experiments of the automatic English question-answering model indicate that its average accuracy was 94.68%, surpassing other models by 4.77%. The average answer time for the four types of questions was 30.52 ms, and the average answer time for the cause questions was 11.45 ms. The results show that the proposed English automatic question-answering model has better accuracy and timeliness of answering questions, and the improved accuracy for weight calculation is better. The English automatic question-answering model integrating word frequency-inverse document frequency and participle algorithm can satisfy the basic needs of teachers and students in online teaching, course question-answering, etc., which is of positive significance for the development of online education in the context of the Internet. |
| first_indexed | 2024-04-25T00:03:10Z |
| format | Article |
| id | doaj.art-e409b999262d4f2e98da198b0fcb8c54 |
| institution | Directory Open Access Journal |
| issn | 2772-9419 |
| language | English |
| last_indexed | 2024-04-25T00:03:10Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj.art-e409b999262d4f2e98da198b0fcb8c542024-03-14T06:17:18ZengElsevierSystems and Soft Computing2772-94192024-12-016200087Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithmsHainan Wang0Department of Foreign Language, Jilin University of Architecture and Technology, Changchun 130114, ChinaOnline network education offers convenience, however, the inefficiency and time-consuming nature of question-answering models negatively impact the demand for online learning. To address this issue, the study puts forward the development of an automatic English question-answering model. The improved model leverages a term frequence-inverse document frequency approach and an unsupervised participle algorithm based on deep learning. The precision and promptness of the question-answering model were enhanced by refining the weighted allocation of the term frequence-inverse document frequency algorithm and the unsupervised word-splitting algorithm. The validation shows that the improved precision rate is 68.14%, which is 34.37% and 50.45% more than the other two methods, respectively. The precision rate, recall rate, and F1 value for semantic similarity calculation improved by 9.23%, 9.22%, and 9.71%, respectively, compared to the traditional method. The validation experiments of the automatic English question-answering model indicate that its average accuracy was 94.68%, surpassing other models by 4.77%. The average answer time for the four types of questions was 30.52 ms, and the average answer time for the cause questions was 11.45 ms. The results show that the proposed English automatic question-answering model has better accuracy and timeliness of answering questions, and the improved accuracy for weight calculation is better. The English automatic question-answering model integrating word frequency-inverse document frequency and participle algorithm can satisfy the basic needs of teachers and students in online teaching, course question-answering, etc., which is of positive significance for the development of online education in the context of the Internet.http://www.sciencedirect.com/science/article/pii/S2772941924000164Term frequence-inverse document frequencyUnsupervised participle algorithmAutomatic question-answering systemEnglish question-answeringSemantic similarity |
| spellingShingle | Hainan Wang Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms Systems and Soft Computing Term frequence-inverse document frequency Unsupervised participle algorithm Automatic question-answering system English question-answering Semantic similarity |
| title | Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms |
| title_full | Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms |
| title_fullStr | Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms |
| title_full_unstemmed | Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms |
| title_short | Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms |
| title_sort | automatic question answering modeling in english by integrating tf idf and segmentation algorithms |
| topic | Term frequence-inverse document frequency Unsupervised participle algorithm Automatic question-answering system English question-answering Semantic similarity |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000164 |
| work_keys_str_mv | AT hainanwang automaticquestionansweringmodelinginenglishbyintegratingtfidfandsegmentationalgorithms |