CVBed: Structuring CVs usingWord Embeddings

Shweta Garg, Sudhanshu S Singh, Abhijit Mishra, Kuntal Dey


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
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
349–354
Language:
URL:
https://www.aclweb.org/anthology/I17-2059
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-2059.pdf