Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion
This addresses the issue of global semantic understanding in NLP tasks, but it is incremental as it builds on existing methods like TF-IDF and word2vec-CNN.
The paper tackled the problem of neural network models for NLP extracting text in a fine-grained way, which hinders global semantic understanding, by proposing a model combining traditional statistical methods and deep learning with multi-model nonlinear fusion, achieving a sentence similarity matching accuracy of 84% and an F1 score of 75%.
Natural language processing (NLP) task has achieved excellent performance in many fields, including semantic understanding, automatic summarization, image recognition and so on. However, most of the neural network models for NLP extract the text in a fine-grained way, which is not conducive to grasp the meaning of the text from a global perspective. To alleviate the problem, the combination of the traditional statistical method and deep learning model as well as a novel model based on multi model nonlinear fusion are proposed in this paper. The model uses the Jaccard coefficient based on part of speech, Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec-CNN algorithm to measure the similarity of sentences respectively. According to the calculation accuracy of each model, the normalized weight coefficient is obtained and the calculation results are compared. The weighted vector is input into the fully connected neural network to give the final classification results. As a result, the statistical sentence similarity evaluation algorithm reduces the granularity of feature extraction, so it can grasp the sentence features globally. Experimental results show that the matching of sentence similarity calculation method based on multi model nonlinear fusion is 84%, and the F1 value of the model is 75%.