Natural Language Processing for Human Resources: A Survey (2025)

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.

## introduction human resources (hr) is a vital component of any organization, responsible for managing its most valuable resource-people. over the years, computational tools have transformed hr processes like hiring, training, and administration, reshaping the labor market and workplace. at the same time, concerns about the accuracy and fairness of automated systems have also garnered significant attention, paving the way for ongoing and future research. advancements in natural language processing (nlp), especially with large language models (llms), have spurred interest in applying language technologies to a broad range of real-world problems, and the hr domain is no exception. however, this domain remains relatively underrepresented in the nlp research community. 1 as breakthroughs in llms continue to advance various aspects of nlp, key challenges in the hr domain, such as the complexity of processing heterogeneous data, and the scarcity of publicly available data resources, may be alleviated in the coming years. therefore, the hr domain holds substantial potential for growth and also presents unique challenges that can drive nlp research forward. to facilitate this transformation, it is essential to develop a comprehensive overview of key hr activities from an nlp perspective and examine how upstream tasks, such as skill extraction, contribute to downstream applications like job matching. in this paper, we analyze hr activities through the lens of nlp research, categorizing them into key areas and examining how nlp techniques have been applied, along with remaining challenges (figure 1 ). 2 we explore fundamental tasks like information extraction and text classification ( §3), and their role in supporting core applications such as recommendation, language generation, and interaction ( §4). finally, we highlight underrepresented areas in nlp to guide future research ( §5). by organizing the discussion around nlp research topics, our goal is to provide insights for two audiences: this section briefly describes hr activities and their links to nlp. broadly, these activities can be categorized into pre-hiring and post-hiring tasks. pre-hiring: the pre-hiring process for recruiters includes drafting job postings, selecting candidates, conducting interviews, and extending offers. for job seekers, it involves exploring market trends, pursuing necessary training, preparing resumes, applying for jobs, preparing for interviews, and negotiating offers. these tasks rely heavily on nuanced domain-specific knowledge and are closely related to language generation (e.g., writing job postings and resumes, text-based communication) and specialized dialogue (e.g., interviews). post-hiring: key hr functions include setting role requirements aligned with organizational goals, evaluating performance, optimizing team dynamics, and maintaining positive work environments. these tasks are complex, demanding occupationspecific insights and integration of diverse data sources like employee records, organizational network, and textual communications. the application of nlp techniques for these activities faces several challenges: (1) diverse entities and language expressions in hr data, such as the concise, bullet-pointed style of resumes or performance feedback, which vary across industries (e.g., software development vs. culinary arts). the following sections review existing research on hr activities, organized by nlp topics, with a focus on upstream tasks ( §3) and downstream tasks ( §4). we then discuss underrepresented hr activities ( §5) that could benefit from recent nlp advancements (figure 2 ). ## upstream tasks upstream hr tasks aim to enrich raw text corpora through information extraction and classification to facilitate knowledge-intensive downstream tasks. ## conclusion and future directions in this paper, we have categorized critical research challenges within the hr domain and identified significant opportunities for future exploration. to inspire future research in this domain and the broader nlp community, we provide a list of papers and public data resources on github, foot_8 which we plan to update regularly. toward broader goals: the hr domain encompasses a variety of specialized problems where nlp techniques have been successfully applied (e.g., skill extraction). these problems are often tied to broader goals, such as matching talent with appropriate job opportunities and optimizing employee productivity. for example, accurate skill extraction can significantly improve job recommender systems. to accurately extract this skill information, it is useful to perform semantic analysis of documents to identify relevant sections and understand job titles. intermediate tasks like these can improve system performance in downstream applications and provide detailed information that can improve the fairness and transparency of final outcomes. the orchestration of specialized nlp tools to perform complex tasks is increasingly gaining the interest of the research community (e.g., @xcite . the hr domain would benefit from exploring holistic approaches, which could also provide research opportunities to push the boundaries of language technologies. knowledge transfer: some successful research in the hr domain has introduced techniques and knowledge transferable to problems in other applications or domains. this trend is particularly evident in studies on job recommendation and bias mitigation, where the hr domain has established a strong position within the research community. we can also see similar knowledge transfer in some other specialized domains. for instance, the ecommerce domain has been one of the key drivers of multiple core nlp areas such as information extraction, sentiment analysis, and summarization. promoting knowledge transfer to other domains will be key to conducting impactful nlp research in hr in the future.