Papers by Mario Mezzanzanica
SkiLLens: Recognising and Mapping Novel Skills from Millions of Job Ads Across Europe Using Language Models (2026.eacl-industry)
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| Challenge: | Online job ads (OJAs) provide a real-time view of changing demands but require first retrieving skill mentions from unstructured text and then solving the entity linking problem of connecting them to standardized skill taxonomies. |
| Approach: | They propose a multilingual human-in-the-loop pipeline that extracts candidate skills from national OJA corpora using country-specific word embeddings. |
| Outcome: | The proposed pipeline enables timely, multilingual monitoring of emerging skills, supporting agile policy-making and targeted training initiatives. |
Contrastive Explanations of Text Classifiers as a Service (2022.naacl-demo)
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| Challenge: | ContrXT provides time contrastive explanations of black box text classifiers by manipulating binary decision diagrams. |
| Approach: | They propose a system that provides time contrastive explanations of black box classifiers as a service by manipulating binary decision diagrams. |
| Outcome: | The proposed system has a throughput of 2.55 users per second and is available as a python pip package. |
RE-FIN: Retrieval-based Enrichment for Financial data (2025.coling-industry)
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| Challenge: | Financial sentiment analysis (FSA) is a powerful tool to support business decision-making and perform financial forecasting. |
| Approach: | They propose a system that retrieves information from a knowledge base to enrich financial sentences, making them more knowledge-dense and explicit. |
| Outcome: | The proposed system generates propositions from the knowledge base and employs Retrieval-Augmented Generation (RAG) to augment the original text with relevant information. |
ITALIC: An Italian Culture-Aware Natural Language Benchmark (2025.naacl-long)
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| Challenge: | ITALIC is a large-scale benchmark dataset of 10,000 multiple-choice questions designed to evaluate the natural language understanding of the Italian language and culture. |
| Approach: | They propose to use a large-scale benchmark dataset to evaluate the natural language understanding of the Italian language and culture. |
| Outcome: | The ITALIC dataset spans 12 domains and uses 17 state-of-the-art LLMs to assess the natural language understanding of the italian language and culture. |