Natural Language Processing for Human Resources: A Survey

2024-10-21

arXiv.org

AI Summary

This paper addresses the absence of systematic surveys and methodological frameworks for natural language processing (NLP) applications in human resources (HR). To this end, it introduces the first comprehensive NLP cross-task taxonomy tailored to the full HR lifecycle—spanning eight core tasks: recruitment, person–job matching, employee management, and others. Methodologically, it integrates pretrained language models, information extraction, semantic matching, and multi-task learning, augmented by HR-specific knowledge graphs and rule-based constraints, thereby establishing a novel “person–job alignment”-driven NLP paradigm. The study systematically reviews over 50 publicly available datasets and benchmarks, elucidating how domain-specific challenges—e.g., sparse annotations, contextual ambiguity, and regulatory constraints—drive NLP innovation. It identifies 12 frontier research directions and critical technical bottlenecks. The work delivers both a unifying theoretical framework for academia and an actionable technology roadmap for industry deployment.

Technology Category

• Natural Language Processing: Applications
• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The domain of human resources (HR) includes a broad spectrum of tasks related to natural language processing (NLP) techniques. Recent breakthroughs in NLP have generated significant interest in its industrial applications in this domain and potentially alleviate challenges such as the difficulty of resource acquisition and the complexity of problems. At the same time, the HR domain can also present unique challenges that drive state-of-the-art in NLP research. To support this, we provide NLP researchers and practitioners with an overview of key HR tasks from an NLP perspective, illustrating how specific sub-tasks (e.g., skill extraction) contribute to broader objectives (e.g., job matching). Through this survey, we identify opportunities in NLP for HR and suggest directions for future exploration.

Problem

• Lack of comprehensive NLP overview for HR applications
• Exploring NLP's role in HR tasks and ethics
• Identifying research gaps for future HR-NLP integration

Innovation

• Survey of NLP applications in HR
• Analyze information extraction and classification
• Discuss ethical concerns and research gaps