Mario Mezzanzanica

TL;DR: This paper presents WoLMIS, a system aimed at collecting and automatically classifying multilingual Web job vacancies with respect to a standard taxonomy of occupations, which allows analysts and Labor Market specialists to make sense of Labor Market dynamics and trends of several countries in Europe.

TL;DR: This paper presents the approach for automatically classifying million Web job vacancies on a standard taxonomy of occupations through machine learning, and shows how this problem has been expressed in terms of text classification via machine learning.

TL;DR: In this article, a set of innovative tools for labor market intelligence by applying machine learning techniques to web vacancies on the Italian labor market is developed, which allows to calculate, for each occupation, the different types of skills required by the market alongside with relevant variables such as region, sector, education and level of experience.

TL;DR: This paper applies and compares several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.

TL;DR: The experimental outcomes show the effectiveness of the model-based approach for data quality as they provide a fine-grained analysis of both the source dataset and the cleansing procedures, enabling domain experts to identify the most relevant quality issues as well as the action points for improving the cleansing activities.