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CareerCorpus: A comprehensive dataset of annotated resumes
Md Sagor Chowdhury1, Adiba Fairooz Chowdhury1, Ayesha Banu1
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology (CUET), Chattogram, Bangladesh.
Data in Brief
|April 17, 2026
Summary
The CareerCorpus dataset offers 302 annotated resumes for multi-class classification across six job categories. This resource aids in resume classification and skill extraction for recruitment analytics.
Area of Science:
- Natural Language Processing
- Data Science
- Information Retrieval
Background:
- Resume data is crucial for recruitment and career development.
- Existing datasets may lack diversity in occupational categories or annotation quality.
- Automated resume analysis requires robust, well-annotated datasets.
Purpose of the Study:
- Introduce the CareerCorpus dataset, a novel collection of 302 annotated resumes.
- Support multi-class classification tasks for resume categorization.
- Facilitate research in skill extraction, recruitment analytics, and natural language processing.
Main Methods:
- Collected resumes from Kaggle and LinkedIn across six occupational categories.
- Ensured dual annotation by domain experts with relevant professional/academic backgrounds.
- Preprocessed data including HTML-to-text conversion, PII removal, and text normalization.
Main Results:
- Dataset covers Teacher, Finance, Apparel, Accountant, Banking, and Research Assistant roles.
- Inter-annotator agreement varied by category, with Apparel showing the highest (r=0.89).
- Dataset released in Excel format (.xlsx) under a CC-BY-4.0 license via Mendeley Data.
Conclusions:
- CareerCorpus provides a valuable resource for advancing automated resume analysis.
- The dataset's dual annotations allow for analysis of annotation uncertainty.
- Enables diverse applications including resume classification, skill extraction, and recruitment analytics.
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