Graph Convolution Text Matching for Resume Job Description
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Solution Overview
Problem
Manual matching of resume and job descriptions is inefficient and subjective, and existing text matching technologies like textCNN struggle to accurately capture co-occurrence information and relationships between words, leading to inaccurate semantic expression of features.
Innovation Solution
A method involving determining a word set and semantic units from a text set, generating a graph based on term frequency-inverse document frequency and pointwise mutual information, and using a graph convolution model to produce a final feature representation for accurate text matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching of resume and job descriptions is used, then personal experience and judgment are applied, but the process is inefficient and subjective
Solution Approach 1:
The patent replaces manual mechanical matching with an automated text matching system using deep learning models (Bi-GRU, attention mechanisms, and graph convolutional networks) to objectively analyze and compare resume and job description texts, eliminating human subjectivity while maintaining high accuracy
Solution Approach 2:
The patent transforms the matching process by changing from manual evaluation parameters to automated textual feature parameters including word co-occurrence frequencies, semantic relationships, and graph-based structural features, enabling efficient computational analysis
2Productivity
If existing text matching technologies like textCNN are used, then automated text processing is achieved, but co-occurrence information and word relationships cannot be accurately captured
Solution Approach 1:
The patent adds a graph structure dimension to the traditional text processing pipeline, representing words as nodes and co-occurrence relationships as edges with weights, thereby capturing global word relationships that linear models like textCNN cannot detect
Solution Approach 2:
The patent combines multiple feature extraction approaches (TF-IDF, pointwise mutual information, graph convolution features, and Bi-GRU sequence features) to create a composite feature representation that comprehensively captures both local and global text semantics
Data Source
AI summary
A method, an apparatus, a device, a storage medium and a program product of performing a text matching are provided, which relate to a field of a computer technology, and in particular to natural language processing and deep learning technologies. The method includes: determining a word set and a plurality of semantic units from a text set, the word set is associated with a first predetermined attribute, and the text set contains a plurality of first texts indicating an object information and a plurality of second texts indicating an object demand information; generating a graph; and generating a final feature representation associated with the text set and the word set based on the graph and a graph convolution model, so as to perform the text matching.