Text matching method and device, equipment, storage medium and program product
A text and text collection technology, applied in the field of natural language processing and deep learning technology, can solve problems with personal subjective color and low efficiency, and achieve the effect of accurate semantic expression ability
Active Publication Date: 2021-06-22
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Problems solved by technology
[0002] In the application field of text matching, such as resume and job matching, it is usually used to manually analyze resume information and job description information, and judge the matching degree of resume and job based on personal experience, which is inefficient and has a personal subjective color
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[0020] Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
[0021] As mentioned above, in the application field that uses manual document matching, such as using manual matching of personnel resumes and job positions, it is inefficient and subjective. In a traditional scheme, the resume and job description text can be represented by textCNN (text convolutional neural network), but it is difficult for textCNN to capture the co-oc...
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The invention discloses a text matching method and device, equipment, a storage medium and a program product, and relates to the technical field of computers, in particular to the field of natural language processing and deep learning. The specific implementation scheme is as follows: determining a word set and a plurality of semantic units associated with a first predetermined attribute from a text set, wherein the text set comprises a plurality of first texts for indicating object information and a plurality of second texts for indicating object demand information; generating a graph, the weight of the edge between any text node and word node in the graph being based on the word frequency-inverse document frequency of the word represented by the word node relative to the text represented by the text node and the text set, and the weight of an edge between any two word nodes in the graph being based on point mutual information of two words represented by the two word nodes relative to the semantic units; and based on the graph and the graph convolution model; and generating a final feature representation of the text set and the word set for text matching. Therefore, the text features can have more accurate semantic expression ability.
Description
technical field [0001] The present disclosure relates to the field of computer technology, in particular to natural language processing and deep learning technologies. Background technique [0002] In the application field of text matching, such as resume and job matching, it is usually used to manually analyze resume information and job description information, and judge the matching degree of resume and job based on personal experience, which is inefficient and subjective. Contents of the invention [0003] The present disclosure provides a method, device, device, storage medium and program product for text matching. [0004] According to a first aspect of the present disclosure, there is provided a method for text matching, comprising: determining a word set associated with a first predetermined attribute from a text set and determining point mutual information between words in the word set A plurality of semantic units, the text set includes a plurality of first texts...
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IPC IPC(8): G06F40/194G06F40/289G06F40/30G06K9/62G06N3/04G06N3/08G06Q10/10
CPCG06F40/194G06F40/30G06F40/289G06N3/08G06Q10/105G06N3/045G06F18/22G06F40/284G06F40/237G06F18/29G06V30/153G06V30/18181G06V30/41G06V30/274
Inventor 马超张敬帅黄启帆姚开春王鹏祝恒书
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD