Abstract
Automatic analysis of curriculum vitae (CVs) of applicants is of tremendous importance in recruitment scenarios. The semi-structuredness of CVs, however, makes CV processing a challenging task. We propose a solution towards transforming CVs to follow a unified structure, thereby, paving ways for smoother CV analysis. The problem of restructuring is posed as a section relabeling problem, where each section of a given CV gets reassigned to a predefined label. Our relabeling method relies on semantic relatedness computed between section header, content and labels, based on phrase-embeddings learned from a large pool of CVs. We follow different heuristics to measure semantic relatedness. Our best heuristic achieves an F-score of 93.17% on a test dataset with gold-standard labels obtained using manual annotation.- Anthology ID:
- I17-2059
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 349–354
- Language:
- URL:
- https://aclanthology.org/I17-2059
- DOI:
- Cite (ACL):
- Shweta Garg, Sudhanshu S Singh, Abhijit Mishra, and Kuntal Dey. 2017. CVBed: Structuring CVs usingWord Embeddings. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 349–354, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- CVBed: Structuring CVs usingWord Embeddings (Garg et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/I17-2059.pdf