In order to improve the status quo of relying on human labor for document management of credit in-formation in construction credit management,This paper proposed a text categorization method based on Natural Language Processing(NLP)for the information of construction enterprise's bad credit information.Firstly,the word vector represen-tation of the text was obtained based on Skip-Gram model using labeled data and a large number of unlabeled;secondly,the Bi-directional Long-Short Term Memory Network(BiLSTM),which incorporated the Attention-Mechanism,was used to perform feature extraction and text classification on the labeled data.The results showed that:in small-sample training,using a larger corpus to train the word vector model could effectively improve the classification performance of the text clas-sification model,the NLP-based text classification method could realize the fast and automatic classification of the informa-tion about the bad Credit information of construction enterprises.
bad credit informationadministrative penaltySkip-Gram word vectorAttention-Mechanismtext classification
维普
万方数据
