Reports
Resume Parsing as Hierarchical Sequence Labeling: An Empirical Study
Extracting information from a resume is usually a two-stage process. Our ML team proposes using a neural network architecture to tackle both stages simultaneously. The model improves system efficiency and resume parsing accuracy in 7 languages.
Reports
Normalization of Education Information in Digitalized Recruitment Processes
In this paper, our team normalizes education information extracted from resumes by transforming it into level/field of study. They also define a new taxonomy for fields of study and show how the process can be applied to candidate-job matching.