Natural Language Processing for Human Resources: A Survey

• URL: http://arxiv.org/abs/2410.16498v2
• Date: Tue, 25 Mar 2025 00:49:15 GMT
• Title: Natural Language Processing for Human Resources: A Survey
• Authors: Naoki Otani, Nikita Bhutani, Estevam Hruschka,
• Abstract summary: Advances in Natural Language Processing have the potential to transform HR processes.<n>This paper discovers opportunities for researchers and practitioners to harness NLP's transformative potential in this domain.
• Score: 7.234532661418072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advances in Natural Language Processing (NLP) have the potential to transform HR processes, from recruitment to employee management. While recent breakthroughs in NLP have generated significant interest in its industrial applications, a comprehensive overview of how NLP can be applied across HR activities is still lacking. This paper discovers opportunities for researchers and practitioners to harness NLP's transformative potential in this domain. We analyze key fundamental tasks such as information extraction and text classification, and their roles in downstream applications like recommendation and language generation, while also discussing ethical concerns. Additionally, we identify gaps in current research and encourage future work to explore holistic approaches for achieving broader objectives in this field.

Related papers

• From Text to Talent: A Pipeline for Extracting Insights from Candidate Profiles [44.38380596387969]
  This paper proposes a novel pipeline that leverages Large Language Models and graph similarity measures to suggest ideal candidates for specific job openings. Our approach represents candidate profiles as multimodal embeddings, enabling the capture of nuanced relationships between job requirements and candidate attributes.
  arXiv  Detail & Related papers  (2025-03-21T16:18:44Z)
• The Nature of NLP: Analyzing Contributions in NLP Papers [77.31665252336157]
  We quantitatively investigate what constitutes NLP research by examining research papers. Our findings reveal a rising involvement of machine learning in NLP since the early nineties. In post-2020, there has been a resurgence of focus on language and people.
  arXiv  Detail & Related papers  (2024-09-29T01:29:28Z)
• Challenges and Opportunities of NLP for HR Applications: A Discussion Paper [13.584222421057696]
  Machine learning and natural language processing have opened up vast areas of potential application use cases. We review the use cases for text analytics in the realm of human resources/personnel management.
  arXiv  Detail & Related papers  (2024-05-13T14:09:06Z)
• The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey [6.516561905186376]
  The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP) We study the inherent challenges associated with extending context length and present an organized overview of the existing strategies employed by researchers. We explore whether there is a consensus within the research community regarding evaluation standards and identify areas where further agreement is needed.
  arXiv  Detail & Related papers  (2024-01-15T18:07:21Z)
• Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
  This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
  arXiv  Detail & Related papers  (2023-11-22T02:45:01Z)
• The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions [114.67699010359637]
  We analyze a large-scale collection of real user queries to GPT. We find that tasks such as design'' and planning'' are prevalent in user interactions but are largely neglected or different from traditional NLP benchmarks.
  arXiv  Detail & Related papers  (2023-10-19T02:12:17Z)
• Text Analysis Using Deep Neural Networks in Digital Humanities and Information Science [0.934612743192798]
  Deep neural networks (DNNs) dominate the field of automatic text analysis and natural language processing (NLP) DNNs are the state-of-the-art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research. Using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation.
  arXiv  Detail & Related papers  (2023-07-30T12:54:39Z)
• Exploring the Landscape of Natural Language Processing Research [3.3916160303055567]
  Several NLP-related approaches have been surveyed in the research community. A comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.
  arXiv  Detail & Related papers  (2023-07-20T07:33:30Z)
• Efficient Methods for Natural Language Processing: A Survey [76.34572727185896]
  This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.
  arXiv  Detail & Related papers  (2022-08-31T20:32:35Z)
• Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
  Deep learning has been the mainstream technique in natural language processing (NLP) area. Deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
  arXiv  Detail & Related papers  (2022-05-03T13:58:38Z)
• Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19 [58.720142291102135]
  We discuss ways in which current and future NLP approaches can be made more inclusive by covering low-resource languages. We suggest several future directions for researchers interested in maximizing the positive societal impacts of NLP.
  arXiv  Detail & Related papers  (2021-08-11T12:54:26Z)
• A Survey of Data Augmentation Approaches for NLP [12.606206831969262]
  Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. We present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.
  arXiv  Detail & Related papers  (2021-05-07T06:03:45Z)
• Low-Resource Adaptation of Neural NLP Models [0.30458514384586405]
  This thesis investigates methods for dealing with low-resource scenarios in information extraction and natural language understanding. We develop and adapt neural NLP models to explore a number of research questions concerning NLP tasks with minimal or no training data.
  arXiv  Detail & Related papers  (2020-11-09T12:13:55Z)
• Low-resource Languages: A Review of Past Work and Future Challenges [68.8204255655161]
  A current problem in NLP is massaging and processing low-resource languages which lack useful training attributes such as supervised data, number of native speakers or experts, etc. This review paper concisely summarizes previous groundbreaking achievements made towards resolving this problem, and analyzes potential improvements in the context of the overall future research direction.
  arXiv  Detail & Related papers  (2020-06-12T15:21:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.