@inproceedings{garg-etal-2017-cvbed,
title = "{CVB}ed: Structuring {CV}s using{W}ord Embeddings",
author = "Garg, Shweta and
Singh, Sudhanshu S and
Mishra, Abhijit and
Dey, Kuntal",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2059",
pages = "349--354",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T CVBed: Structuring CVs usingWord Embeddings
%A Garg, Shweta
%A Singh, Sudhanshu S.
%A Mishra, Abhijit
%A Dey, Kuntal
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F garg-etal-2017-cvbed
%X 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.
%U https://aclanthology.org/I17-2059
%P 349-354
Markdown (Informal)
[CVBed: Structuring CVs usingWord Embeddings](https://aclanthology.org/I17-2059) (Garg et al., IJCNLP 2017)
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.