@inproceedings{sierra-martinez-etal-2020-enhancing,
title = "Enhancing Job Searches in {M}exico City with Language Technologies",
author = "Sierra Mart{\'\i}nez, Gerardo and
Bel-Enguix, Gemma and
G{\'o}mez-Adorno, Helena and
Torres Moreno, Juan Manuel and
Hern{\'a}ndez-Garc{\'\i}a, Tonatiuh and
Guadarrama-Olvera, Julio V and
Ortiz-Barajas, Jes{\'u}s-Germ{\'a}n and
Rojas, {\'A}ngela Mar{\'\i}a and
Damerau, Tomas and
Arag{\'o}n Mart{\'\i}nez, Soledad",
editor = "Samy, Doaa and
P{\'e}rez-Fern{\'a}ndez, David and
Arenas-Garc{\'\i}a, Jer{\'o}nimo",
booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lt4gov-1.3",
pages = "15--21",
abstract = {In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagn{\'o}stico de Competencias Demandadas), a system developed by Mexico City{'}s Ministry of Labor and Employment Promotion (STyFE: Secretar{\'\i}a de Trabajo y Fomento del Empleo de la Ciudad de M{\'e}xico) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingenier{\'\i}a Ling{\"u}{\'\i}stica) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe{'}s job vacancies system{'}s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.},
language = "English",
ISBN = "979-10-95546-62-7",
}
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<abstract>In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagnóstico de Competencias Demandadas), a system developed by Mexico City’s Ministry of Labor and Employment Promotion (STyFE: Secretaría de Trabajo y Fomento del Empleo de la Ciudad de México) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingeniería Lingüística) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe’s job vacancies system’s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.</abstract>
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%0 Conference Proceedings
%T Enhancing Job Searches in Mexico City with Language Technologies
%A Sierra Martínez, Gerardo
%A Bel-Enguix, Gemma
%A Gómez-Adorno, Helena
%A Torres Moreno, Juan Manuel
%A Hernández-García, Tonatiuh
%A Guadarrama-Olvera, Julio V.
%A Ortiz-Barajas, Jesús-Germán
%A Rojas, Ángela María
%A Damerau, Tomas
%A Aragón Martínez, Soledad
%Y Samy, Doaa
%Y Pérez-Fernández, David
%Y Arenas-García, Jerónimo
%S Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-62-7
%G English
%F sierra-martinez-etal-2020-enhancing
%X In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagnóstico de Competencias Demandadas), a system developed by Mexico City’s Ministry of Labor and Employment Promotion (STyFE: Secretaría de Trabajo y Fomento del Empleo de la Ciudad de México) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingeniería Lingüística) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe’s job vacancies system’s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.
%U https://aclanthology.org/2020.lt4gov-1.3
%P 15-21
Markdown (Informal)
[Enhancing Job Searches in Mexico City with Language Technologies](https://aclanthology.org/2020.lt4gov-1.3) (Sierra Martínez et al., LT4Gov 2020)
ACL
- Gerardo Sierra Martínez, Gemma Bel-Enguix, Helena Gómez-Adorno, Juan Manuel Torres Moreno, Tonatiuh Hernández-García, Julio V Guadarrama-Olvera, Jesús-Germán Ortiz-Barajas, Ángela María Rojas, Tomas Damerau, and Soledad Aragón Martínez. 2020. Enhancing Job Searches in Mexico City with Language Technologies. In Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov), pages 15–21, Marseille, France. European Language Resources Association.