A Study on Fast Classification of Electronic Information Text Based on CNN-BiLSTM Networks
Traditional text categorization methods often have slow processing speed when dealing with large-scale and complex electronic information texts,resulting in poor text categorization.Aiming at the above problems,a research on fast classification of electronic information text based on CNN-BiLSTM network is proposed.First,the similarity of electronic information texts is calculated,which can be realized by comparing the semantic similarity between texts.Next,the feature weights of the text vectors are obtained,and the keywords and phrases in the text are extracted and their importance is calculated by using natural language processing techniques.Then,CNN extracts the local features in the text and BiLSTM captures the long distance dependencies in the text.By combining these two networks,the feature vectors of the text are better extracted.Finally,the minimum distance from the text to the center of the vectors is judged to quickly classify electronic information text.The experiment proves that the method can quickly deal with large-scale and complex electronic information text,and the text classification effect is good.
CNNBiLSTM networkelectronic informationfast text classification
国家科技期刊平台
NSTL
万方数据
