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A candidate answer selection method based on deep learning

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

Inactive Publication Date: 2021-01-01
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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  • A candidate answer selection method based on deep learning
  • A candidate answer selection method based on deep learning
  • A candidate answer selection method based on deep 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 present invention provides a method for selecting candidate answers based on deep learning, comprising: step S1, inputting question sentences and candidate answers, and analyzing them into question sentence word sequences and candidate answer word sequences respectively; step S2, pairing question sentences through long short-term memory network The word sequence and candidate answer word sequence are modeled to obtain the semantic representation of the question sentence and the semantic representation of the candidate answer; step S3, select the word vector of the word with the highest weight value in the question sentence word sequence to initialize the knowledge memory module; step S4, according to The knowledge information stored in the knowledge memory module and the semantic representation of the question are calculated, and the knowledge representation of the question is calculated; step S5, the similarity between the knowledge representation of the question and the semantic representation of the candidate answer is calculated, and the candidate answer with the highest similarity is selected output. The present invention introduces a knowledge memory module into the deep learning network to improve the connection between questions and candidate answers, improve the quality of answer selection, and be better applied to community question-and-answer websites and question-and-answer systems.

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 Patents(China)
IPC IPC(8): G06F16/33G06F40/30G06N3/04G06N3/08
CPCG06N3/08G06F40/30G06N3/045
Inventor 杨燕安炜杰贺樑
Owner EAST CHINA NORMAL UNIV