Cloud Recruitment False Information Detection Method Based on Entity Bias and BERT-BiLSTM

DOI

10.2991/978-94-6463-304-7_56How to use a DOI?

Keywords

cloud recruitment; false recruitment detection; entity bias; BERT; Bi-LSTM

Abstract

The Internet has swept the world, and the way of job hunting has undergone earth-shaking changes. Cloud recruitment has become the mainstream of the times. However, with the development of cloud recruitment, non-hair elements publish false recruitment information online, which induces job seekers to be deceived and their money is empty. Monitoring false recruitment information is helpful for people to identify false recruitment in advance and cut off the conditions for fraud at the source. One of the difficulties in monitoring false recruitment information is feature extraction, and extracting effective features from recruitment information is the focus of subsequent detection. The other difficulty is that the time effect of high-frequency information entities makes the model lack generalization ability. Therefore, we propose a BERT-BiLSTM model without entity deviation, which can effectively improve the detection ability and generalization ability of the model while fully extracting information context features. The experimental results show that our model has reached 99.02% accuracy and F1 score of 0.92.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Peiwen Gao
AU  - Liang Zhang
PY  - 2023
DA  - 2023/12/04
TI  - Cloud Recruitment False Information Detection Method Based on Entity Bias and BERT-BiLSTM
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 541
EP  - 547
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_56
DO  - 10.2991/978-94-6463-304-7_56
ID  - Gao2023
ER  -