计算机工程2017，Vol.43Issue(3)：181-186,6.DOI:10.3969/j.issn.1000-3428.2017.03.031
基于字符级循环网络的查询意图识别模型
Query Intention Recognition Model Based on Character Level Cyclic Network
摘要
Abstract
Intention recognition methods,which are mainly based on feature template,have complicated hand-crafted feature extraction process and are difficult to capture semantic information of texts.Aiming at this problem,this paper proposes a new query intention identification model based on character level recurrent network.In order to effectively extract deep semantic features of a sentence and decrease long distance information dependent constraints,this paper uses Long Short-Term Memory Neural Network(LSTM) as a linear transformation of neural network layer,and uses a reverse LSTM layer to extract future information character.To avoid error propagation problem caused by inaccurate word segmentation results,it uses Chinese characters as inputs of the model,and uses distributed representation of characters to improve extractions of semantic features of sentences.Experimental results show that the method has an accuracy of 90.7%,which is higher than the characteristics template method,and it can improve the classification performance of user query intention.关键词
查询意图/字符级/循环神经网络/记忆网络/词向量Key words
query intention/character level/Recurrent Neural Network(RNN)/memory network/word vector分类
信息技术与安全科学引用本文复制引用
孟奎,刘梦赤,胡婕..基于字符级循环网络的查询意图识别模型[J].计算机工程,2017,43(3):181-186,6.基金项目
国家自然科学基金(61202100). （61202100）
