Abstract: To construct the intelligent manufacturing Question-Answering (QA) system,facilitate the knowledge sharing of intelligent manufacturing and accelerate the intelligent manufacturing industrial layout,the deep learning algorithm was used to improve the problems that the construction process of the traditional QA System was too complicated,the requirement of human intervention and prior knowledge was too high,the questions and answers could not be effectively mapped and so on.Long Short-Term Memory (LSTM) deep neural network algorithm was applied to avoid the gradient disappearance and gradient explosion problems of the general deep learning algorithm.The gate mechanism in LSTM algorithm could avoid the influence of chain rule on gradient over-optimization and analyze the semantics of sentence directly.The similarity calculation was used to determine the correct answer or not.The verification experiments on the validation set showed that the proposed method of semantic analysis had a clear improvement in the accuracy of QA System.

Key words: deep learning, intelligent manufacturing, Encoder-Decoder frame, question-answering system, long short-term memory, gate mechanism, similarity

摘要： 为了构建智能制造知识问答系统,促进智能制造知识传递,加快智能制造产业布局,利用深度学习算法对传统问答系统构建流程过于复杂、所需手工与先验知识要求过高、问题与答案无法有效映射等问题进行改进。采用长短记忆神经网络算法来避免一般深度学习算法在进行梯度优化时的梯度消失与梯度爆炸问题,算法中的门机制能够消除链式法则对梯度过度优化的影响,直接对句子的语义做出解析,并利用相似度计算判别回答的正确与否。通过在评测集上的验证实验表明,该语义解析方法能够显著提升问答系统的准确率。

关键词: 深度学习, 智能制造, Encoder-Decoder框架, 问答系统, 长短记忆神经网络, 门机制, 相似度