In recent years, word embeddings (WEs) have proven relevant for studying differences and similarities among job professions and skills required by the labour market across countries, providing valuable insights about the labour market dynamics to support policy and decision-making. In such a scenario, aligning WEs constructed across different countries and languages becomes key to allowing experts to reason on the labour market, catching technological and cultural shifts across borders. This paper proposes MEAL, an unsupervised method for aligning monolingual embeddings. Our approach selects a seed lexicon of anchors, i.e. words with the same meaning in both corpora that will be used as pivots in the alignment, without assuming a priori semantic similarities. Indeed, unlike previous literary works, to asses this relationship MEAL takes into account the semantic similarity between the neighbour of the two words in the WE space. Particularly, it chooses optimal anchors that are less susceptible to meaning shift. We deploy MEAL within the research framework of a European H-2020 Project that aims to use AI technologies to predict the future of the European labour market. Specifically, we apply it to the embeddings we train on 7+ millions of Online Job Advertisements (OJAs) collected in 2022. As a main outcome, MEAL allows stakeholders and policymakers (i) to estimate job similarities in Online Labour Markets across Europe, facilitating the assessment of how well these markets align with the taxonomy outlined by the official European Skills and Competences taxonomy, and (ii) to obtain indicators to support a data-driven policy design at a very fine-grained territorial level.

Explaining how two machine learning classification models differ in their behaviour is gaining significance in eXplainable AI, given the increasing diffusion of learning-based decision support systems. Human decision-makers deal with more than one machine learning model in several practical situations. Consequently, the importance of understanding how two machine learning models work beyond their prediction performances is key to understanding their behaviour, differences, and likeness. Some attempts have been made to address these problems, for instance, by explaining text classifiers in a time-contrastive fashion. In this paper, we present MERLIN, a novel eXplainable AI approach that provides contrastive explanations of two machine learning models, introducing the concept of model-contrastive explanations. We propose an encoding that allows MERLIN to work with both text and tabular data and with mixed continuous and discrete features. To show the effectiveness of our approach, we evaluate it on an extensive set of benchmark datasets. MERLIN is also implemented as a python-pip package.

We propose JoTA (Job Taxonomy Alignment), a domain-independent, knowledge-poor method for automatic taxonomy alignment of lexical taxonomies via word embeddings. JoTA associates all the leaf terms of the origin taxonomy to one or many concepts in the destination one, employing a scoring function, which merges the score of a hierarchical method and the score of a classification task. JoTA is developed in the context of an EU Grant aiming at bridging the national taxonomies of EU countries towards the European Skills, Competences, Qualifications and Occupations taxonomy (ESCO) through AI. The method reaches a 0.8 accuracy on recommending top-5 occupations and a wMRR of 0.72.

In the last few years, we have been witnessing the increasing deployment of machine learning-based systems, which act as black boxes whose behaviour is hidden to end-users. As a side-effect, this contributes to increasing the need for explainable methods and tools to support the coordination between humans and ML models towards collaborative decision-making. In this paper, we demonstrate ContrXT, a novel tool that computes the differences in the classification logic of two distinct trained models, reasoning on their symbolic representation through Binary Decision Diagrams. ContrXT is available as a pip package and API.

We demonstrate NEO, a tool for automatically enriching the European Occupation and Skill Taxonomy (ESCO) with terms that represents new occupations extracted from million Online Job Advertisements (OJAs). NEO proposes (i) a novel metric that allows one to measure the semantic similarity between words in a taxonomy, and (ii) a set of measures that estimate the adherence of new terms to the most suited taxonomic concept, enabling the user to evaluate the suggestions. To test its effectiveness, NEO has been evaluated over 2M+ 2018 UK job ads, along with a user-study to confirm the usefulness of NEO in the taxonomy enrichment task.

While word embeddings have been showing their effectiveness in capturing semantic and lexical similarities in a large number of domains, in case the corpus used to generate embeddings is associated with a taxonomy (i.e., classification tasks over standard de-jure taxonomies) the common intrinsic and extrinsic evaluation tasks cannot guarantee that the generated embeddings are consistent with the taxonomy. This, as a consequence sharply limits the use of distributional semantics in those domains. To address this issue, we design and implement MEET, which proposes a new measure -HSS- that allows evaluating embeddings from a text corpus preserving the semantic similarity relations of the taxonomy.

We propose a recommender system that, starting from a set of users skills, identifies the most suitable jobs as they emerge from a large text of Online Job Vacancies (OJVs). To this aim, we process 2.5M+ OJVs posted in three different countries (United Kingdom, France and Germany), generating several embeddings and performing an intrinsic evaluation of their quality. Besides, we compute a measure of skill importance for each occupation in each country, the Revealed Comparative Advantage (rca). The best vector models, together with the rca, are used to feed a graph database, which will serve as the keystone for the recommender system. Finally, a user study of 10 validates the effectiveness of Skills2Job, both in terms of precision and nDGC.

In this paper, we present Skills2Graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics - computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) - and distributional semantics - generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by Skills2Graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by Skills2Graph.

The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors.Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving.This study, instead of proposing any "new model", investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain.We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors.These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.