A Short Text Sensitive Word Recognition Method Based on Bi-LSTM Neural Network
In order to accurately identify and process sensitive words,a short text sensitive word recognition method based on bidirectional long short term memory(Bi-LSTM)neural network was proposed to address the issues of high segmentation delay and low recognition accuracy.By analyzing the sensitive lexicon,the sensitive lexicon was divided into two categories and three levels,and the short text interference information(special characters,traditional characters and split Chinese characters)was preprocessed.The Bi-LSTM neural network was introduced to construct a short text segmentation model.The optimal parame-ters were determined by secondary training,and the sensitivity values of words were calculated repeatedly.Through the sensitivity comparison function,the short text sensitive words were extracted,and the sensitive lexicon was matched to determine the catego-ry and level of sensitive words,so as to realize the recognition of short text sensitive words.The experimental results showed that in different experimental groups,the short text segmentation delay obtained by applying the method proposed in this paper is lower than the given maximum limit,and the recognition accuracy of sensitive words in short text is higher than 84.42%,indicating better application performance.
short textsensitive word recognitiontext filteringedit distancebidirectional long short-term memory neu-ral network
NETL
NSTL
万方数据
维普
