Method for predicting referenced number of paper by utilizing review opinions based on deep learning

A deep learning and cited technology, applied in neural learning methods, prediction, unstructured text data retrieval, etc., can solve the problems of ignoring the text information of review comments and only using papers, etc., and achieve the effect of rich semantics and accurate prediction effect

Active Publication Date: 2019-12-20
RENMIN UNIVERSITY OF CHINA
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Problems solved by technology

Although the above studies have made great progress in this task, they only used the information of the paper itself and the author, ignoring the important textual information of the review comments

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  • Method for predicting referenced number of paper by utilizing review opinions based on deep learning
  • Method for predicting referenced number of paper by utilizing review opinions based on deep learning
  • Method for predicting referenced number of paper by utilizing review opinions based on deep learning

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Embodiment Construction

[0019] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0020] The present invention will be further explained below in combination with specific embodiments.

[0021] like figure 2 As shown, the present invention provides a method based on deep learning to predict the number of citations of papers using review opinions, which is characterized in that the method uses depth components and width components to jointly train paper review opinions; for a paper, Contains three parts of information, namely summary text a d , the review opinions of K reviewers and the...

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Abstract

The invention provides a method for predicting the referenced number of a paper by utilizing review opinions based on deep learning. The method comprises the following steps: training the review opinions of the paper by utilizing a deep component and a width component; the deep component comprising an abstract-comment matching mechanism and a cross comment matching mechanism and being used for learning deep features of review opinions; firstly, extracting comments related to an abstract through the abstract-comment matching mechanism, and removing information irrelevant to the quoted number ofa prediction paper; then, the cross comment matching mechanism capturing the consistency and diversity among different review opinions so as to describe the interaction among a plurality of reviewers; meanwhile, integrating width features through the width assembly; and finally, predicting the referenced number of the paper by using the combination of the depth component and the width component.According to the method, the semantic information in the review opinions is deeply described, semantic representation is enriched, and prediction of the reference number of the paper is more accurateby mining the text information of the review opinions.

Description

technical field [0001] The present invention relates to the technical field of methods for predicting the number of citations of papers, and in particular to a method for predicting the number of citations of papers based on review opinions based on deep learning. modeling. Background technique [0002] In recent years, the number of academic papers has been increasing significantly. The number of papers submitted and accepted by the academic conference EMNLP in 2018 has reached 2,231 and 549, respectively. For such a large number of academic papers, how to effectively evaluate the impact of scientific literature is a long-term research challenge. A typical way to evaluate the influence of an academic paper is to reflect the influence of the paper in the research community through the number of citations after the paper is published. [0003] Therefore, many researchers have invented methods to predict the number of citations of a paper to measure the influence of the pape...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06F16/38G06F16/34G06N3/04G06N3/08
CPCG06Q10/04G06F16/382G06F16/345G06N3/08G06N3/045
Inventor 赵鑫李思晴文继荣
Owner RENMIN UNIVERSITY OF CHINA
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