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Citation difference matching method, device and storage medium based on mg-lstm

A matching method and citation technology, applied in neural learning methods, text database query, unstructured text data retrieval and other directions, can solve the problems of low discovery efficiency, difficult to guarantee accuracy, large workload, etc. The effect of ensuring the screening accuracy

Active Publication Date: 2022-05-20
CENT SOUTH UNIV
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Problems solved by technology

[0004] The present invention provides a citation difference matching method, device and storage medium based on MG-LSTM (Multi-granularity Long Short-Term Memory, multi-granularity Long Short-Term Memory Network), to solve the problem of low efficiency and work problems in related technologies. The problem is that the volume is large and the accuracy is difficult to guarantee

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  • Citation difference matching method, device and storage medium based on mg-lstm
  • Citation difference matching method, device and storage medium based on mg-lstm
  • Citation difference matching method, device and storage medium based on mg-lstm

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Embodiment 1

[0059] In order to realize the purpose of the present invention, it is first necessary to build and train a citation difference recognition model, and the specific process is as follows.

[0060] Citations include metadata such as titles, authors, publishers, etc. Since different citations are composed of different metadata and have different text characteristics, this embodiment uses two types of word embedding and character embedding, and the corresponding The title, author, and publisher are segmented at the granularity of words or characters, and then their sequences are mapped to a low-dimensional vector space. In this embodiment, the title metadata is taken as an example for specific description. First, the title metadata of the citation to be screened and the credible citation with converted to caption embedding vectors, respectively with Caption embedding vector with Form the title embedding vector pair, with The elements in are the embedded representati...

Embodiment 2

[0094] This embodiment provides a citation difference matching device based on MG-LSTM, including:

[0095] Data acquisition module: used to acquire the title, author and publisher metadata of citations to be screened and credible citations;

[0096] Granular Segmentation Module: It is used to divide and convert the title, author, and publisher metadata of the citations to be screened and credible citations into title embedding vector pairs, author embedding vector pairs, and publishing house embedding vector pairs using words and characters as the segmentation granularity. ;

[0097] Embedded vector weighting module: used to learn the weights of the title embedding vector pair, author embedding vector pair, and publisher embedding vector pair respectively based on the attention mechanism, and update the title embedding vector pair, author embedding vector pair, and publishing house embedding vector pair based on the corresponding weights right;

[0098] Citation difference ...

Embodiment 3

[0106] This embodiment provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded by a processor and executing the MG-LSTM-based citation difference matching method as described in Embodiment 1.

[0107] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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Abstract

The present invention discloses a citation difference matching method, device and storage medium based on MG-LSTM, wherein the method includes: acquiring title, author, and publishing house metadata of citations to be screened and credible citations; using words and characters as segmentation granularity , the title, author, and publisher metadata of the citations to be screened and credible citations are divided and converted into title embedding vector pairs, author embedding vector pairs, and publishing house embedding vector pairs; the weights of each embedding vector pair are learned based on the attention mechanism , and update each embedding vector pair based on the corresponding weight; input the updated embedding vector pair into the pre-trained citation difference recognition model, and output the citation difference matching result category. Fine-grained screening of citations can be carried out to determine the type of difference in the citations; the introduction of the attention mechanism can better characterize the relationship between the metadata of the citations to be screened and the credible citations, and then combine with the bidirectional LSTM network to simultaneously retain two The characteristic information of the direction ensures the discrimination accuracy.

Description

technical field [0001] The present invention relates to the technical field of citation difference identification, in particular to an MG-LSTM-based citation difference matching method, device and storage medium. Background technique [0002] In recent years, with the continuous increase of the country's investment in scientific research, the number of applications for various science fund projects has also hit new highs, and a large amount of citation data is included in the project applications. In the face of massive citation data, relying on managers to discover or solicit reports from the society, the workload is huge, the efficiency is low, and the accuracy is difficult to guarantee, so it is difficult to meet the actual needs. [0003] Citation screening refers to the authenticity of the citation data in the fund project application, and provides auxiliary support for the applicant's preliminary research foundation evaluation in the fund project form review. There ar...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/33G06F40/258G06F40/284G06F40/289G06N3/04G06N3/08
CPCG06F40/289G06F40/258G06F40/284G06F16/3331G06N3/049G06N3/08G06N3/044
Inventor 王也龙军章成源魏翔翔杨展
Owner CENT SOUTH UNIV
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