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.- Anthology ID:
- L14-1615
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2076–2082
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/791_Paper.pdf
- DOI:
- Cite (ACL):
- Marc Poch, Núria Bel, Sergio Espeja, and Felipe Navío. 2014. Ranking Job Offers for Candidates: learning hidden knowledge from Big Data. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2076–2082, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Ranking Job Offers for Candidates: learning hidden knowledge from Big Data (Poch et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/791_Paper.pdf