Research on Text Similarity Based on Interactive Features and Multi-scale Features
Aiming at the problem of low accuracy of similarity calculation results caused by lack of information transmission and neglecting multiple semantic information in the process of text similarity analysis,a novel text similarity model based on interactive features and multi-scale features was proposed by combining bidirectional long short-term memory(BiLSTM).Firstly,BiLSTM was used to encode the sentences and extract the global feature matrix,and the soft attention mechanism and cosine similarity were used to interact with the feature matrix respectively,so as to transfer the semantic information inside the two groups of feature matrices.Secondly,the two groups of interaction features were weighted to synthesize all interactive information,and BiLSTM was used to re-encode the weighted interactive features and the initial coding features to capture the difference information between the features.Thirdly,multiple semantic information of differential information were extracted by multi-scale convolution and channel attention was combined to enhance significant feature information.Finally,two sets of enhanced features were fused to judge whether the text pairs are similar.Two data sets were selected to verify the proposed method,and Fl values of the proposed model reached the highest values of 88.15％and 85.03％,which is better than that of other methods.
text similaritybidirectional long short-term memoryinteractive featuresmulti-scale featureschannel attention
万方数据
维普
