Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (5): 126-135.doi: 10.3969/j.issn.1000-5641.2022.05.011

• Construction and Analysis of Supply Chain Knowledge Graph • Previous Articles    

Text matching based on multi-dimensional feature representation

Ming WANG, Te LI, Dingjiang HUANG*()   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2022-07-20 Accepted:2022-07-20 Online:2022-09-25 Published:2022-09-26
  • Contact: Dingjiang HUANG


Text semantic matching is the basis of many natural language processing tasks. Text semantic matching techniques are required in many scenarios, such as search, question, and answer systems. In practical application scenarios, the efficiency of text semantic matching is crucial. Although the representational learning semantic-matching model is less accurate than the interactive model, it is more efficient. The key to improve the performance of learning-based semantic-matching models is to extract sentence vectors with high-level semantic features. On this basis, this paper presents the design of a feature-fusion module and feature-extraction module based on the ERINE model to obtain sentence vectors with multidimensional semantic features. Further, the performance of the model is improved to obtain semantic information by designing a loss function of semantic prediction. Finally, the accuracy on the Baidu Qianyan dataset reaches 0.851, which indicates good performance.

Key words: text matching, pre-training model, sentence embeddings, semantic features

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