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Candidate answer selection method based on depth learning

A candidate answer, deep learning technology, applied in neural learning methods, text database query, unstructured text data retrieval, etc.

Inactive Publication Date: 2019-01-04
EAST CHINA NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the improvement of people's demand for the accuracy of information acquisition, traditional search technology can no longer meet people's needs

Method used

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  • Candidate answer selection method based on depth learning
  • Candidate answer selection method based on depth learning
  • Candidate answer selection method based on depth learning

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Embodiment Construction

[0048] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar structures in the drawings, and thus their repeated descriptions will be omitted.

[0049] The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the invention. However, those skilled in the art will appreciate that the technical solutions of the present invention may be practiced without one or more of the specific...

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Abstract

The invention provides a method for selecting candidate answers based on depth learning, which comprises the following steps: step S1, inputting a question and a candidate answers, respectively resolving the question and the candidate answers into a question word sequence and a candidate answer word sequence; step S2, model that question word sequence and the candidate answer word sequence throughthe long-short time memory network to obtain the semantic representation of the question sentence and the semantic representation of the candidate answer; step S3, selecting that word vector of the word with the highest weight value in the question sentence word sequence to initialize the knowledge memory module; step S4, calculating the knowledge representation of the question according to the knowledge information stored in the knowledge memory module and the semantic representation of the question; Step S5, calculating the similarity between the knowledge representation of the question andthe semantic representation of the candidate answer, and selecting the candidate answer with the highest similarity to output. The invention introduces a knowledge memory module in the depth learningnetwork to improve the connection between the question sentence and the candidate answer, and to improve the quality of the answer selection, so as to be better applied to the community question answering website and the question answering system.

Description

technical field [0001] The present invention relates to the technical field of deep learning, question answering system and answer selection, in particular to a method for selecting candidate answers based on deep learning. Background technique [0002] With the improvement of people's demand for the accuracy of information acquisition, traditional search technology can no longer meet people's needs. The community Q&A system enables users to post questions and ask questions to meet their own information needs, and at the same time communicate and share their experience, knowledge and experience with other users. A large number of user question answering data resources have been accumulated in the community question answering system. How to make good use of these resources to better meet the information needs of users is a major problem currently being studied by research institutions and industries. [0003] Specifically, a user's newly submitted question in the community q...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F17/27G06N3/04G06N3/08
CPCG06N3/08G06F40/30G06N3/045
Inventor 杨燕安炜杰贺樑
Owner EAST CHINA NORMAL UNIV
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