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

Dong, Xiangjue (Spring 2021)

Permanent URL: https://etd.library.emory.edu/concern/etds/bc386k49h?locale=it
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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

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