Text matching based on multi-dimensional feature representation

doi:10.3969/j.issn.1000-5641.2022.05.011

Abstract

Abstract:

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

CLC Number:

TP391

Ming WANG, Te LI, Dingjiang HUANG. Text matching based on multi-dimensional feature representation[J]. Journal of East China Normal University(Natural Science), 2022, 2022(5): 126-135.

Figures/Tables 5

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模型	准确率
baseline	0.753
baseline + 语义预测 loss	0.752
baseline + 特征融合	0.823
baseline + 特征融合 + 特征提取	0.838
baseline + 特征融合 + 特征提取 + 语义预测 loss	0.852

模型	准确率
本文模型	0.853
Nezha^[22]-based Model (交互型、单模型)	0.840
ERINE^[16]-based Model (交互型、单模型)	0.881

模型	单对文本的匹配时间/ms	1000个文本检索/ms	10000个文本检索/ms
余弦相似度	0.594	594.000	5940.000
本文模型	133.261	729.667	6072.667
ERINE-based Model(交互型、单模型)	126.376	126376	1263760