基于预训练语言模型的COVID-19综述文本分类

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) Pub Date : 2022-01-21 DOI:10.1109/ICPECA53709.2022.9719020

Juxing Di, Zixu Liu, Yang Yang

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引用次数: 2

摘要

本实验分析了中国慈善会官方提供的10万条疫情相关微博。采用知识集成增强表示(Enhanced Representation through Knowledge Integration, ERNIE)方法，提高了预训练模型对汉语语义信息提取的效果。之后，为了节省计算成本，将深度金字塔网络(DPCNN)与ERNIE合并。增强的长距离文本特征提取性能。该模型在六个情绪三类任务的比较测试中最为有效，使BERT预训练模型的准确率提高了7%。

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Text classification of COVID-19 reviews based on pre-training language model

This experiment analyzed 100,000 epidemic-related microblogs officially provided by the CCF. Using Enhanced Representation through Knowledge Integration (ERNIE), the effect of pre-training model on extracting Chinese semantic information was improved. After that, the deep pyramid network (DPCNN) was merged with ERNIE to save computing costs. Enhanced feature extraction performance for long-distance text. This model was the most effective in the comparison test of six emotional three-category tasks, which improved the accuracy of BERT pre-training model by 7%.

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2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)

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