A study of an Improved Clustering Method for Fault-oriented Short Texts
In the field of aeronautical manufacturing,in order to solve the fault data with low quality and mostly short text,this paper pin proposes a text clustering method.Firstly,the method extracts the global se-mantic information of the text through the AutoEncoder module,then extracts the key semantic information in the text through the key semantic extraction module,and finally fuses the two extracted features to perform text clustering using K-Means.The method effectively solves the problems of losing semantic information and over-reliance on raw data quality in the training process of traditionalAutoEncoder.Experiments show that the clustering effect of the method proposed in this paper is better than the existing clustering algorithms,and the clustering results also prove the importance of key semantic information for text clustering.
text clusteringauto encoderK-Meanskey semanticsfeature fusion
万方数据
维普
