Advertisement

Research on Template-Based Factual Automatic Question Answering Technology

  • Wenhui Hu
  • Xueyang LiuEmail author
  • Chengli Xing
  • Minghui Zhang
  • Sen Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)

Abstract

With the increase of people’s demand for information retrieval, question answering system as a new generation of retrieval methods has attracted more and more attention. This paper proposes an automated template generation method based on remote supervision. In the process of generating template, in order to identify the keywords express the semantics of the problem, this paper designs an algorithm to mapping entity relationship to keywords by using pattern mining and statistical methods. This method implements automation of template generation, and makes the templates more accurate. For the template matching, by Deep-learning-based template matching algorithm, this paper use vector to represent words, and designs a tree-based convolutional neural network to extract features of the dependency syntax information. Building a neural network model for template prediction. Experimental results show that the above method can effectively improve the matching accuracy compared with the traditional template matching method.

Keywords

Question answering system Knowledge graph Template generation Template matching Neural network 

Notes

Acknowledgement

Supported by the 2018 project Research and Application of Key Technologies on Online Monitoring, Efficient Operation and Maintenance and Intelligent Evaluation of Health Condition of Electrical Equipment’ in state Power Grid Corporation (No. PDB17201800280), and Major State Research Development Program of China (No. 2016QY04W0804).

References

  1. 1.
    Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to Answer Open-Domain Questions. arXiv preprint arXiv:1704.00051
  2. 2.
    Fader, A., Zettlemoyer, L., Etzioni, O.: Paraphrase-driven learning for open question answering. In: Meeting of the Association for Computational Linguistics, pp. 1608–1618 (2013)Google Scholar
  3. 3.
    Lopez, V., Tommasi, P., Kotoulas, S., Wu, J.: QuerioDALI: question answering over dynamic and linked knowledge graphs. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 363–382. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46547-0_32CrossRefGoogle Scholar
  4. 4.
    Bast, H., Haussmann, E.: More accurate question answering on freebase. In: ACM International on Conference on Information and Knowledge Management, pp. 1431–1440. ACM (2015)Google Scholar
  5. 5.
    Abujabal, A., Yahya, M., Riedewald, M., et al.: Automated template generation for question answering over knowledge graphs. In: International World Wide Web Conferences Steering Committee, pp. 1191–1200 (2017)Google Scholar
  6. 6.
    Berant, J., Chou, A., Frostig, R., et al.: Semantic parsing on freebase from question-answer Pairs. In: Proceedings of EMNLP (2013)Google Scholar
  7. 7.
    Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. Comput. Sci. (2014)Google Scholar
  8. 8.
    Joshi, M., Sawant, U., Chakrabarti, S.: Knowledge graph and corpus driven segmentation and answer inference for telegraphic entity-seeking queries. In: Conference on Empirical Methods in Natural Language Processing, pp. 1104–1114 (2014)Google Scholar
  9. 9.
    Cranias, L., Papageorgiou, H., Piperidis, S.: A matching technique in example-based machine translation. In: Conference on Computational Linguistics, pp. 100–104. Association for Computational Linguistics (1994)Google Scholar
  10. 10.
    Ding, C.H.Q.: A similarity-based probability model for latent semantic indexing. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 58–65. ACM (1999)Google Scholar
  11. 11.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336 (1998)Google Scholar
  12. 12.
    Nirenburg, S., Domashnev, C., Grannes, D.J.: Two approaches to matching in example-based machine translation. In: TMI, pp. 47–57 (1993)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wenhui Hu
    • 1
  • Xueyang Liu
    • 1
    Email author
  • Chengli Xing
    • 2
  • Minghui Zhang
    • 3
  • Sen Ma
    • 1
  1. 1.National Engineering Research Center for Software EngineeringPeking UniversityBeijingChina
  2. 2.SiChuan Tianfu Bank Co., Ltd.NanchongChina
  3. 3.Handan Institute of InnovationPeking UniversityHandan, BeijingChina

Personalised recommendations