R\'esum\'e Parsing as Hierarchical Sequence Labeling: An Empirical Study

TL;DR

This paper presents a hierarchical sequence labeling approach for résumé parsing, demonstrating its effectiveness across multiple languages and analyzing model performance and deployment trade-offs.

Contribution

It introduces a novel hierarchical sequence labeling model for résumé parsing and provides multilingual corpora and comprehensive experimental evaluation.

Findings

Proposed models outperform previous approaches in information extraction accuracy.

Multilingual corpora enable cross-language résumé parsing evaluation.

Analysis of model efficiency informs deployment considerations.

Abstract

Extracting information from r\'esum\'es is typically formulated as a two-stage problem, where the document is first segmented into sections and then each section is processed individually to extract the target entities. Instead, we cast the whole problem as sequence labeling in two levels -- lines and tokens -- and study model architectures for solving both tasks simultaneously. We build high-quality r\'esum\'e parsing corpora in English, French, Chinese, Spanish, German, Portuguese, and Swedish. Based on these corpora, we present experimental results that demonstrate the effectiveness of the proposed models for the information extraction task, outperforming approaches introduced in previous work. We conduct an ablation study of the proposed architectures. We also analyze both model performance and resource efficiency, and describe the trade-offs for model deployment in the context of a production environment.