Analysis of Graph-Based Semi-Structured Categorical Model for Competence-Level Classification Open Access

Abstract

Transformer-based models have been widely used for many natural language processing tasks and shown excellent capability in capturing contextual information, especially for document classification. Many existing transformer-based methods, however, even treat semi-structured text data as a block of text. These methods tend to ignore the hierarchical information and semantic correlations hidden in semi-structured text data, which can be captured by graph-based network models. This paper proposes a novel graph representation of semi-structured resume data that considers the categorical and hierarchical relationship in resumes. Our experiments show that our graph-based models outperform transformer methods for resume classification tasks and show better interpretability and generalization.

Table of Contents

1 Introduction 1

2 Background 3

3 Dataset 5

3.1 Data Processing . . . . . . . . . . . . . . . . . . . . . 5

3.2 Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4 Approach 9

4.1 Graph Construction . . . . . . . . . . . . . . . . . 10

4.2 Context Encoder . . . . . . . . . . . . . . . . . . . . 12

4.3 Graph Classifier . . . . . . . . . . . . . . . . . . . . . . 12

5 Experiments 14

5.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

5.2 Experimental Setups . . . . . . . . . . . . . . . . 15

5.3 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.5 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . 19

6 Conclusion 21

About this Master's Thesis

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School	Laney Graduate School
Department	Computer Science and Informatics
Degree	M.S.
Submission	Master's Thesis
Language	English
Research Field	Computer Science
Keyword	Text Classification
Committee Chair / Thesis Advisor	Choi, Jinho D., Emory University
Committee Members	Sarker, Abeed, Emory University Yang, Carl, Emory University

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