Research on Question-Answering System Based on Deep Learning

  • Bo SongEmail author
  • Yue Zhuo
  • Xiaomei Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


With the continuous development of the network, Question-Answering system has become a way for people to get information quickly. The QA task aims to provide precise and quick answers to user questions from a collection of documents or a database. In this paper, we introduce an attention based deep learning model to match the question and answer sentence. The proposed model employs a bidirectional long-short term memory(BLSTM) to solve the problem of lack features. And we also use the attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model and the results show that our approach outperforms the method of feature construction based on machine learning. And the attention mechanism improves the matching accuracy.


Question-answering system Deep learning BLSTM Attention mechanism 



Project supported by the Basic scientific research projects of colleges and universities in Liaoning Province (2017L317).


  1. 1.
    Abacha, A.B., Zweigenbaum, P.: MEANS: a medical question-answering system combining NLP techniques and semantic web technologies. J. Inf. Process. Manag. 51(5), 570–594 (2015)CrossRefGoogle Scholar
  2. 2.
    Mao, X.L., Li, X.M.: A survey on question and answering systems. J. Front. Comput. Sci. Technol. 06(3), 193–207 (2012)Google Scholar
  3. 3.
    Kim, Y.: Convolutional Neural Networks for Sentence Classification. Eprint Arxiv (2014)Google Scholar
  4. 4.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate. Computer Science (2014)Google Scholar
  5. 5.
    Cho, K., Merrienboer, B.V., Gulcehre, C., et al.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Computer Science (2014)Google Scholar
  6. 6.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to Sequence Learning with Neural Networks. In: NLPS (2014)Google Scholar
  7. 7.
    Rush, A.M., Chopra, S., Weston, J.: A Neural Attention Model for Abstractive Sentence Summarization. Computer Science (2015)Google Scholar
  8. 8.
    Iyyer, M., Boyd-Graber, J., Claudino, L., et al.: A neural network for factoid question answering over paragraphs. In: Conference on Empirical Methods in Natural Language Processing, NIPS (2014)Google Scholar
  9. 9.
    Hu, B., Lu, Z., Li, H., et al.: Convolutional neural network architectures for matching natural language sentences. In: Advances in neural information processing (2015)Google Scholar
  10. 10.
    START Natural Language Question Answering System.
  11. 11.
    Zheng, Z.: AnswerBus question answering system. In: International Conference on Human Language Technology Research. pp. 399–404. Morgan Kaufmann Publishers Inc. (2002)Google Scholar
  12. 12.
    Moldovan, D,I., Harabagiu, S.M., Goodrum, R.A., et al.: LASSO: a tool for surfing the answer net. In: National Institute of Standards and Technology (1999)Google Scholar
  13. 13.
    Vargas-Vera, M., Motta, E.: AQUA – ontology-based question answering system. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 468–477. Springer, Heidelberg (2004). Scholar
  14. 14.
    Song, B., Zhuo, Y., Li, X.: A personalized intelligent tutoring system of primary mathematics based on perl. In: Tan, Y., Shi, Y., Li, L. (eds.) ICSI 2016. LNCS, vol. 9713, pp. 609–617. Springer, Cham (2016). Scholar
  15. 15.
    Zhang, W.: Chinese Question Answering System Technology and Application. Electronic Industry Press, Beijing (2016)Google Scholar
  16. 16.
    Li, J.N.: Research and Implementation of IT Domain Question Answering System. South China University of Technology (2015)Google Scholar
  17. 17.
    Gers, F.A., Schraudolph, N.N.: Learning precise timing with LSTM recurrent networks. (2003)Google Scholar
  18. 18.
    Graves, A.: Generating Sequences With Recurrent Neural Networks. Computer Science (2013)Google Scholar
  19. 19.
    Nie, Y.P., Han, Y., Huang, J.M., et al.: Attention-based encoder-decoder model for answer selection in question answering. J. Front. Inf. Technol. Electr. Eng. 18(4), 535–544 (2017)CrossRefGoogle Scholar
  20. 20.
    Rong, G.H., Huang, Z.H.: Question answer matching method based on deep learning. J. Comput. Appl. 37(10), 286–2865 (2017)Google Scholar
  21. 21.
    Yin, W., Schütze, H., Xiang, B., et al.: ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. Computer Science (2015)Google Scholar
  22. 22.
    Tan, M., Xiang, B., Zhou, B.: LSTM-based Deep Learning Models for non-factoid answer selection. Computer Science (2015)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of SoftwareShenyang Normal UniversityShenyangChina
  2. 2.Research Training Center of Basic EducationShenyangChina

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