Research Article
Text Error Correction Method in the Construction Industry Based on Transfer Learning
@INPROCEEDINGS{10.1007/978-3-030-99200-2_22, author={Zhenguo Hou and Weitao Yang and Haiying He and Peicong Zhang and Ziyu Wang and Xiaosheng Ji}, title={Text Error Correction Method in the Construction Industry Based on Transfer Learning}, proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings}, proceedings_a={CHINACOM}, year={2022}, month={4}, keywords={Text error correction Transfer learning BERT model Multi-domain text}, doi={10.1007/978-3-030-99200-2_22} }- Zhenguo Hou
Weitao Yang
Haiying He
Peicong Zhang
Ziyu Wang
Xiaosheng Ji
Year: 2022
Text Error Correction Method in the Construction Industry Based on Transfer Learning
CHINACOM
Springer
DOI: 10.1007/978-3-030-99200-2_22
Abstract
Text error correction is of great value in the review of texts in the construction industry. For construction industry texts, which are compound texts with multi-domain proper nouns, the lack of labeled data leads to poor error correction algorithms based on deep learning. For this reason, this paper proposes a text error correction method in the construction industry based on transfer learning. Based on the pre-trained BERT model, we transfer some parameters to the target error correction model after unsupervised training by unlabeled related field dataset, and then retrain the model through the training samples of the construction document corpus dataset to obtain better error correction effects. Meanwhile, we dynamically adjust the pre-training task in transfer learning to improve the performance of the word order correction task. Experimental results show that our proposed model has higher precision rate, recall rate and lower false positive rate in the error correction task than other models.
