A Hybrid Bi-attention Mechanisms and Long Short-term Memory Model for Chinese Question Classification

摘要：

　　The question classification assigns a classify label to each question sentence.These category labels represent the answer type of the question or the intention type of user.The classification of question is not only an important part of the question answer(QA)system,but also the result of the question classification can influences the quality of the QA system directly.At early stages,scholars studied rule-based problem classification methods.Due to the rules were not universal,they were gradually transformed into methods based on machine learning and deep learning.This paper proposes a question classification model(BAL)that combines with bi-attention mechanisms and long short-term memory(LSTM)network.The model proposed in this paper can acquire multi-level text features and recognize the classification of questions better by combining with the attention mechanism of the word vector and the attention mechanism of part-of-speech.In the Chinese question classification data set,compared with common convolutional neural network,common long-short-term memory network,and convolutional neural network based on attention mechanisms,the model proposed in this paper can achieve better classification results.

关键词： question classification LSTM attention model

作者: Songjiang Hu Yajun Du Xianglin Luo Dezhi Kong Qing Li

作者单位: School of Computer and Software Engineering,Xihua University,Chengdu 610039,China

会议类型: 国际会议

会议名称: 2018 3rd International Conference on Computer Science and Information Engineering (ICCSIE 2018) 2018第三届计算机科学与信息工程国际会议(ICCSIE 2018)

会议地点: 西安

会议语种:英文

页码: 321-327

在线出版日期: 2018-09-21（万方平台首次上网日期，不代表论文的发表时间）