Chinese Intention Recognition Algorithm Based on BERT-GRU-Capsule Network

Yang Xia, Chaobing Huang
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Abstract

With the continuous development of natural language processing technology and deep learning technology, related technologies in intelligent question answering field have made rapid progress in recent years. In the application of intelligent question answering system, the research of intention recognition algorithm is very important. In fact, intention recognition corresponds to the task of multi-classification of short texts in the field of natural language processing, and the algorithm of intention recognition is directly related to the question answering effect. In this paper, a BERT-GRU-Capsule network is proposed and compared with some classical intent-recognition networks on the user intent-domain classification dataset of SMP2017-ECDT.The experimental results show that the precision, recall and f1 values of the proposed network on the test set reach 0.931, 0.925 and 0.926 respectively, and the experimental results prove that the proposed Chinese intention recognition algorithm is superior to the classical intention recognition algorithm.
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基于BERT-GRU-Capsule网络的汉语意图识别算法
随着自然语言处理技术和深度学习技术的不断发展,智能问答领域的相关技术近年来取得了长足的进步。在智能问答系统的应用中,意图识别算法的研究是非常重要的。实际上,意图识别对应于自然语言处理领域对短文本进行多分类的任务,意图识别的算法直接关系到问答效果。本文在SMP2017-ECDT用户意图域分类数据集上,提出了BERT-GRU-Capsule网络,并与经典意图识别网络进行了比较。实验结果表明,本文提出的网络在测试集上的准确率、查全率和f1值分别达到0.931、0.925和0.926,实验结果证明本文提出的中文意图识别算法优于经典意图识别算法。
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