Open intent detection based on distance loss and decision boundary
In order to solve the problem that the feature distribution is not compact enough due to insufficient processing of feature distribution in open intent detection task, an open intent detection method integrating BERT, distance loss and decision boundary was proposed. Firstly, the context features be...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Hebei University of Science and Technology
2024-12-01
|
| Series: | Journal of Hebei University of Science and Technology |
| Subjects: | |
| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202406006?st=article_issue |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841560002362343424 |
|---|---|
| author | Panpan ZHANG Yu HUA Zhinan GOU Yunxian CHI Kai GAO |
| author_facet | Panpan ZHANG Yu HUA Zhinan GOU Yunxian CHI Kai GAO |
| author_sort | Panpan ZHANG |
| collection | DOAJ |
| description | In order to solve the problem that the feature distribution is not compact enough due to insufficient processing of feature distribution in open intent detection task, an open intent detection method integrating BERT, distance loss and decision boundary was proposed. Firstly, the context features between texts were captured by BERT model. Then, the learning of sample features was made more compact by distance loss. Finally, decision boundary learning was carried out to achieve the task of open intent detection. The results show that the proposed method has high performance on the public dataset StackOverflow, with the best performance under two different known intent ratio settings, achieving the accuracy of 88.28% and 84.43%, and the F1 values of 87.51% and 87.40%, respectively. The research results complement the future representation reprocessing method for boundary detection,and can provide reference for open intent detection. |
| format | Article |
| id | doaj-art-eba7ba3948ad4fb9b493d4775efd9298 |
| institution | Kabale University |
| issn | 1008-1542 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj-art-eba7ba3948ad4fb9b493d4775efd92982025-01-05T06:35:22ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422024-12-0145661862610.7535/hbkd.2024yx06006b202406006Open intent detection based on distance loss and decision boundaryPanpan ZHANG0Yu HUA1Zhinan GOU2Yunxian CHI3Kai GAO4School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Management Science and Information Technology, Hebei University of Economics and Business, Shijiazhuang, Hebei 050061, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaIn order to solve the problem that the feature distribution is not compact enough due to insufficient processing of feature distribution in open intent detection task, an open intent detection method integrating BERT, distance loss and decision boundary was proposed. Firstly, the context features between texts were captured by BERT model. Then, the learning of sample features was made more compact by distance loss. Finally, decision boundary learning was carried out to achieve the task of open intent detection. The results show that the proposed method has high performance on the public dataset StackOverflow, with the best performance under two different known intent ratio settings, achieving the accuracy of 88.28% and 84.43%, and the F1 values of 87.51% and 87.40%, respectively. The research results complement the future representation reprocessing method for boundary detection,and can provide reference for open intent detection.https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202406006?st=article_issuenatural language processing; intent recognition; intent detection; distance loss; decision boundary |
| spellingShingle | Panpan ZHANG Yu HUA Zhinan GOU Yunxian CHI Kai GAO Open intent detection based on distance loss and decision boundary Journal of Hebei University of Science and Technology natural language processing; intent recognition; intent detection; distance loss; decision boundary |
| title | Open intent detection based on distance loss and decision boundary |
| title_full | Open intent detection based on distance loss and decision boundary |
| title_fullStr | Open intent detection based on distance loss and decision boundary |
| title_full_unstemmed | Open intent detection based on distance loss and decision boundary |
| title_short | Open intent detection based on distance loss and decision boundary |
| title_sort | open intent detection based on distance loss and decision boundary |
| topic | natural language processing; intent recognition; intent detection; distance loss; decision boundary |
| url | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202406006?st=article_issue |
| work_keys_str_mv | AT panpanzhang openintentdetectionbasedondistancelossanddecisionboundary AT yuhua openintentdetectionbasedondistancelossanddecisionboundary AT zhinangou openintentdetectionbasedondistancelossanddecisionboundary AT yunxianchi openintentdetectionbasedondistancelossanddecisionboundary AT kaigao openintentdetectionbasedondistancelossanddecisionboundary |