An error correction model of early warning information is developed for semantic errors in the release of national meteorological early warning information.By establishing a national meteorological historical warning information corpus,training an error correction model based on Seq2Seq deep learning method,and verifying it with a rule model based on statistical methods,a quality control platform for monitoring the legitimacy of warning and forecasting information was formed.A quality control business process of"intelligent semantic analysis+manual verification"was constructed to achieve rapid localization and reminder of sensitive words.Since the early warning quality control platform began to be applied,the error rate of information content decreased by 70%compared with the previous year,and the semantic error correction effect was remarkable.
early warning releasesemantic analysisSeq2Seq deep learningearly warning legality monitoringquality control model