Text classification of COVID-19 reviews based on pre-training language model
2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022
; : 1179-1183, 2022.
Article
in English
| Scopus | ID: covidwho-1788729
ABSTRACT
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%. © 2022 IEEE.
Deep learning; Pre-training language model; Text classification; Weibo; Classification (of information); Computational linguistics; Semantics; Text processing; Knowledge integration; Language model; Micro-blog; Pre-training; Pyramid network; Semantics Information; Training model; Social networking (online)
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022
Year:
2022
Document Type:
Article
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