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Question answering algorithm based on deep learning

Published: 31 December 2021 Publication History

Abstract

With the rapid development of Internet technology, people are no longer limited to simply obtaining information from the Internet and then manually screening, but prefer to use high-tech to retrieve information quickly and accurately. Therefore, this paper designs an intelligent question answering system to solve and apply the above problems. At present, intelligent question answering system has become a hot direction in the field of natural language processing. The purpose of this paper is to build a deep learning-based question answering algorithm based on deep learning technology and lay a foundation for its future application.

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Yu Chuanming, Wang Feng, Zhang Zhengang, Kong Lingge, An Lu. Research on Knowledge Base Question Answering Model Based on Representation Learning [J]. Science and Technology Information Research, 2021, 3(01):56--70.
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EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
October 2021
1723 pages
ISBN:9781450384322
DOI:10.1145/3501409
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 December 2021

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Author Tags

  1. answer deep learning
  2. information retrieval
  3. intelligent question
  4. natural language processing

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  • Research-article
  • Research
  • Refereed limited

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EITCE 2021

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EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
Overall Acceptance Rate 508 of 972 submissions, 52%

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