With the development of artificial intelligence, the automatic question answering technology has been paid more and more attention. Along with the technology’s maturing, the problem is gradually exposed. This technique has several major difficulties. There is semantic recognition. How accurate are the answers to the questions? Because Chinese grammar is relatively complex. There are many different ways of saying the same thing. There is a powerful challenge to semantic recognition. Due to poor semantic recognition, the accuracy of the corresponding questions and answers is low. Aiming at this kind of questions, we build a Q & A library for common use of cars. Firstly, TFIDF method is used to build a basic FAQ retrieval system. Then we use BILSTM-siamese network to construct a semantic similarity model. The accuracy rate on the test set was 99.52%. The final FAQ system: first retrieves 30 most similar questions using the TFIDF model, then uses BILSTM-siamese network matching and returns the answer of the most similar question. The system has a lot to be improved. For example, how to combine this system with speech recognition? How to realize speech recognition will be a great challenge.
Dong, Z.T., Bao, Y.Q. and Ma, X.H. (2010) Method for Calculating Similarity of Question Sentences in Intelligent Question Answering System. Journal of Wuhan University of Technology (Information and Management Engineering Edition), 32, 31-34.
Sen, Z., Wang, B. and Jones, G.J.F. (2007) ICT-DCU Question Answering Task at NTCIR-6. Proceedings of the 6th Ntcir Workshop on Research in Information Access Technologies, Tokyo, 15-18 May 2007, 154-167.
Salton, G. and Clement, T.Y. (1973) On the Construction of Effective Vocabularies for Information Retrieval. Proceedings of the 1973 Meeting on Programming Languages and Information Retrieval, Gaithersburg, 4-6 November 1973, 48-60. https://doi.org/10.1145/951762.951766
Yang, Y., Yan, D.B., Xu, M., Wan, L., Li, Q. and Qiu, D. (2019) Clas-sification of 95598 Complaint Work Order Based on Improved TFIDF Feature Weighting Algorithm. Electricity and Energy, 40, 205-207 226.