Reference number : GC-2017-Q4-00914, amendment number : , agreement type : G, recipient legal name : Khomh, Foutse (École Polytechnique de Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : TAS (Techno Aero Services inc.) is a Canadian recruitment agency specialized in aeronautics, engineering, and_x000D_
technology. At TAS, candidates currently submit resumes to a database through a web interface. These resumes_x000D_
are then manually processed by recruiters before a suitable candidate is matched to a position. Through this_x000D_
laborious manual process, great prospective candidates often get lost in the piles. This project aims to leverage_x000D_
text mining and machine learning techniques to automatically collect information about prospective candidates_x000D_
from their submitted resume and public profiles on websites like Linked In, Twitter, and Facebook to provide_x000D_
recruiters with an overall picture of a candidate's strengths and weaknesses. Both the technical and_x000D_
communication skills of the candidates will be analyzed and summarized. The goal is to provide recruiters with_x000D_
the information they need to judge a candidate on both his mastery of key skills and his ability to fit into the_x000D_
company culture._x000D_
To achieve this goal, we will leverage big data processing infrastructure, including machine learning through_x000D_
frameworks like Google Tensorflow, to automatically extract useful information from textual data about_x000D_
prospective candidates. Deep learning approaches have proved to be powerful tools for patterns recognition and_x000D_
classification in diverse problems, such as speech, text, and image recognition. We expect it to also achieve a_x000D_
good performance in automatically detecting useful signal about prospective candidates from data collected_x000D_
about them from their resume and the Web., program purpose french : TAS (Techno Aero Services inc.) is a Canadian recruitment agency specialized in aeronautics, engineering, and_x000D_
technology. At TAS, candidates currently submit resumes to a database through a web interface. These resumes_x000D_
are then manually processed by recruiters before a suitable candidate is matched to a position. Through this_x000D_
laborious manual process, great prospective candidates often get lost in the piles. This project aims to leverage_x000D_
text mining and machine learning techniques to automatically collect information about prospective candidates_x000D_
from their submitted resume and public profiles on websites like Linked In, Twitter, and Facebook to provide_x000D_
recruiters with an overall picture of a candidate's strengths and weaknesses. Both the technical and_x000D_
communication skills of the candidates will be analyzed and summarized. The goal is to provide recruiters with_x000D_
the information they need to judge a candidate on both his mastery of key skills and his ability to fit into the_x000D_
company culture._x000D_
To achieve this goal, we will leverage big data processing infrastructure, including machine learning through_x000D_
frameworks like Google Tensorflow, to automatically extract useful information from textual data about_x000D_
prospective candidates. Deep learning approaches have proved to be powerful tools for patterns recognition and_x000D_
classification in diverse problems, such as speech, text, and image recognition. We expect it to also achieve a_x000D_
good performance in automatically detecting useful signal about prospective candidates from data collected_x000D_
about them from their resume and the Web., agreement title english : Applying Machine Learning Techniques to Automatically Process and Match Candidates Applications to Job Descriptions, agreement title french : Applying Machine Learning Techniques to Automatically Process and Match Candidates Applications to Job Descriptions, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada