@InProceedings{garg-EtAl:2017:I17-2,
  author    = {Garg, Shweta  and  Singh, Sudhanshu S  and  Mishra, Abhijit  and  Dey, Kuntal},
  title     = {CVBed: Structuring CVs usingWord Embeddings},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  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.},
  url       = {http://www.aclweb.org/anthology/I17-2059}
}