基于Bi-LSTM-CRF算法的气象预警信息质控系统的实现

Implementation of Quality Control Systems Based on Bi-LSTM-CRF Algorithm for Meteorological Warning Information

本文采用双向长短期记忆网络条件随机场(Bi-LSTM-CRF)算法,通过双向循环神经网络(Bi-LSTM)对已有的合法预警信息文本数据集和开放域中文分析公开数据集进行训练;采用CRF序列标注法有效地结合了预警前后的标签信息对分词进行序列标注;使用该算法建立的气象预警信息质控系统已应用在安徽省突发事件预警信息发布系统,在实际应用的过程中充分证明基于神经网络的气象预警信息质控系统能直接有效地对新的预警信息中可能含有的敏感字(词)、错别字等进行智能监测,以帮助监测人员进行气象预警判断,从而可以对发布的气象预警信息起到质量把关的作用。

This paper adopts the bi-directional long short-term memory conditional random field (Bi-LSTM-CRF) algorithm to train the existing legal early-warning information database and the open domain Chinese parsing database through the bi-directional long short-term memory.At the same time,the conditional random field (CRF) model is used to label the word segmentation by effectively combining the label information before and after the warning.The quality control system of meteorological early-warning information based on the above algorithm has already been applied in the emergency warning information issuing system of Anhui Province.In the process of practical application,it has been proved that such system can directly and effectively monitor sensitive keywords and misspellings in the upcoming warning information,so as to help monitoring stuff make better judgments and play an important role in the quality controls of the issued weather warning information.