@inproceedings{43228635f25449a7acf9a831b56186ba,
title = "Combining Label-wise Attention and Adversarial Training for Tag Prediction of Web Services",
abstract = "Tagging is well regarded as one of the best ways of managing web services, in which keywords are assigned by users to describe the published services. As users are required to select multiple tags from a large set of candidate tags based on their own understanding, such user-attached tags are not always reliable and may affect the efficiency of service discovery. To alleviate the issue, tag prediction can suggest users appropriate tags for web services based on the textual descriptions of their functionality. Therefore, it is necessary to design tag prediction methods to support service search and recommendation. In this work, we propose a tag prediction model that adopts BERT-based label-wise attention mechanism, and use adversarial training to further improve the model performance. Experimental results on the service datasets collected from ProgrammableWeb show that the proposed method can achieve better prediction performance than other state-of-art methods.",
keywords = "BERT Fine-tuning, Deep Learning, Generative Adversarial Training, Label-wise Attention, Tag Prediction, Web Services",
author = "Qunbo Wang and Wenjun Wu and Yongchi Zhao and Yuzhang Zhuang and Yanni Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Web Services, ICWS 2021 ; Conference date: 05-09-2021 Through 11-09-2021",
year = "2021",
doi = "10.1109/ICWS53863.2021.00054",
language = "英语",
series = "Proceedings - 2021 IEEE International Conference on Web Services, ICWS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "358--363",
editor = "Chang, \{Carl K.\} and Ernesto Damiani and Jing Fan and Parisa Ghodous and Michael Maximilien and Zhongjie Wang and Robert Ward and Jia Zhang",
booktitle = "Proceedings - 2021 IEEE International Conference on Web Services, ICWS 2021",
address = "美国",
}