1. 基于数据增强的 MRC 水利领域命名实体识别模型研究.
- Author
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朱永明 and 邢丹艳
- Abstract
The recognition of named entities in the field of water conservancy is of great significance for the building of water conservancy knowledge graphs and intelligent question answering systems. However, in the current field of water conservancy, there are shortcomings in named entity recognition, such as a lack of annotated corpus, low recognition accuracy of traditional methods and inability to solve polysemous entities. Aiming at the characteristics of water conservancy texts, a Named Entity Recognition Model for Machine Reading Comprehension (MRC) based on data (vocabulary and entity type labels) enhancement, namely the MRC-WLE model was put forward. Mainly, the vocabulary feature information and entity type label feature information in water conservancy texts were injected into the model as "knowledge". It introduced models such as BERT-CRF, BERT-CRF-Word, BERT-BILSTM-CRF and BERT-BILSTM-CRF-Word as controls to evaluate the performance of the MRC-WLE model. The results show that compared with the BERT-CRF and other models mentioned above, the micro average F1 value of the MRC-WLE model has been improved. Compared with the MRC model, the micro average F1 value of the MRC-WLE model has been increased by 0.85%, reflecting the effectiveness of data augmentation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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