TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction
Tourism named entity recognition is indispensable in tourism information extraction, and plays a crucial role in constructing tourism knowledge map and enhancing tourism knowledge quiz system. The difficulty of tourism named entity recognition lies in its complex nested structure, and the lengthy en...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000018 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849766266769768448 |
|---|---|
| author | Kai Gao Jiahao Zhou Yunxian Chi Yimin Wen |
| author_facet | Kai Gao Jiahao Zhou Yunxian Chi Yimin Wen |
| author_sort | Kai Gao |
| collection | DOAJ |
| description | Tourism named entity recognition is indispensable in tourism information extraction, and plays a crucial role in constructing tourism knowledge map and enhancing tourism knowledge quiz system. The difficulty of tourism named entity recognition lies in its complex nested structure, and the lengthy entity naming length. To address these existing problems, we propose a tourism named entity recognition model that jointly predicts entity boundaries, adopting a training strategy of data preprocessing to enhance the model’s ability for tourism named entity boundary recognition, while our model introduces a pre-trained Bert model as well as BiLSTM coding to enhance the representation of the model’s contexts, and uses a combined predictor of Biaffine and MLP to enhance the model’s recognition performance for boundaries, as well as introducing label smoothing cross entropy to smooth the target labels during the training process. Experiments are conducted on three datasets with different granularities. From the analysis of the experimental results, it can be seen that the named entity recognition method achieves higher accuracy and F1 value compared with the optimal baseline model, and also proves the effectiveness and generality of the modeling method proposed in this paper. |
| format | Article |
| id | doaj-art-a94df3a040f647c18f0ae505e172b1ad |
| institution | DOAJ |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-a94df3a040f647c18f0ae505e172b1ad2025-08-20T03:04:38ZengElsevierIntelligent Systems with Applications2667-30532025-03-012520047510.1016/j.iswa.2025.200475TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint predictionKai Gao0Jiahao Zhou1Yunxian Chi2Yimin Wen3School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China; Guangxi Key Laboratory of Culture and Tourism Smart Technology, Guilin Tourism University, Guilin, 541006, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, ChinaGuangxi Key Laboratory of Culture and Tourism Smart Technology, Guilin Tourism University, Guilin, 541006, China; Guangxi Key Laboratory of Image and Graphic intelligent processing, Guilin University of Electronic Technology, Guilin, 541004, China; Corresponding author.Tourism named entity recognition is indispensable in tourism information extraction, and plays a crucial role in constructing tourism knowledge map and enhancing tourism knowledge quiz system. The difficulty of tourism named entity recognition lies in its complex nested structure, and the lengthy entity naming length. To address these existing problems, we propose a tourism named entity recognition model that jointly predicts entity boundaries, adopting a training strategy of data preprocessing to enhance the model’s ability for tourism named entity boundary recognition, while our model introduces a pre-trained Bert model as well as BiLSTM coding to enhance the representation of the model’s contexts, and uses a combined predictor of Biaffine and MLP to enhance the model’s recognition performance for boundaries, as well as introducing label smoothing cross entropy to smooth the target labels during the training process. Experiments are conducted on three datasets with different granularities. From the analysis of the experimental results, it can be seen that the named entity recognition method achieves higher accuracy and F1 value compared with the optimal baseline model, and also proves the effectiveness and generality of the modeling method proposed in this paper.http://www.sciencedirect.com/science/article/pii/S2667305325000018Natural Language ProcessingTourism Named Entity RecognitionEntity boundary recognitionJoint prediction |
| spellingShingle | Kai Gao Jiahao Zhou Yunxian Chi Yimin Wen TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction Intelligent Systems with Applications Natural Language Processing Tourism Named Entity Recognition Entity boundary recognition Joint prediction |
| title | TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction |
| title_full | TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction |
| title_fullStr | TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction |
| title_full_unstemmed | TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction |
| title_short | TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction |
| title_sort | tourismner a tourism named entity recognition method based on entity boundary joint prediction |
| topic | Natural Language Processing Tourism Named Entity Recognition Entity boundary recognition Joint prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000018 |
| work_keys_str_mv | AT kaigao tourismneratourismnamedentityrecognitionmethodbasedonentityboundaryjointprediction AT jiahaozhou tourismneratourismnamedentityrecognitionmethodbasedonentityboundaryjointprediction AT yunxianchi tourismneratourismnamedentityrecognitionmethodbasedonentityboundaryjointprediction AT yiminwen tourismneratourismnamedentityrecognitionmethodbasedonentityboundaryjointprediction |