Summary of the paper
| Title | Ranking Job Offers for Candidates: Learning Hidden Knowledge from Big Data |
|---|---|
| Authors | Marc Poch, Núria Bel, Sergio Espeja and Felipe Navio |
| Abstract | This paper presents a system for suggesting a ranked list of appropriate vacancy descriptions to job seekers in a job board web site. In particular our work has explored the use of supervised classifiers with the objective of learning implicit relations which cannot be found with similarity or pattern based search methods that rely only on explicit information. Skills, names of professions and degrees, among other examples, are expressed in different languages, showing high variation and the use of ad-hoc resources to trace the relations is very costly. This implicit information is unveiled when a candidate applies for a job and therefore it is information that can be used for learning a model to predict new cases. The results of our experiments, which combine different clustering, classification and ranking methods, show the validity of the approach. |
| Topics | Text Mining, Document Classification, Text categorisation |
Full paper  |
Ranking Job Offers for Candidates: Learning Hidden Knowledge from Big Data |
| Bibtex | @InProceedings{POCH14.791, author = {Marc Poch and Núria Bel and Sergio Espeja and Felipe Navio}, title = {Ranking Job Offers for Candidates: Learning Hidden Knowledge from Big Data}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |