REVIEW 2 cited by
Natural Language Processing for Human Resources: A Survey
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 2 Pith papers
-
TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management
TalentCLEF 2026 will run two shared tasks—job-person matching and job-skill matching with skill type labels—on synthetic multilingual hiring data, but the paper presents no results.
-
NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching
On the TalentCLEF 2025 benchmark, zero-shot prompting gave the best multilingual job-title matching result, while fine-tuned classification gave the best job-skill prediction result.
Discussion (0). Continue with ORCID to comment.