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NEO: A System for Identifying New Emerging Occupation from Job Ads

Autor: Giabelli, A, Malandri, L, Mercorio, F, Mezzanzanica, M, Seveso, A

Publikováno v: Proceedings of the AAAI Conference on Artificial Intelligence. 35:16035-16037

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 o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd70919f54a3eddf89bdf6532b1193d2
https://doi.org/10.1609/aaai.v35i18.18004

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Embeddings Evaluation Using a Novel Measure of Semantic Similarity

Autor: Fabio Mercorio, MARIO MEZZANZANICA, Lorenzo Malandri, Anna Giabelli, Navid Nobani

Lexical taxonomies and distributional representations are largely used to support a wide range of NLP applications, including semantic similarity measurements. Recently, several scholars have proposed new approaches to combine those resources into un

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7b7c6c492aac86d42232bf8cc509b46
http://hdl.handle.net/10281/344508

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JoTA: Aligning Multilingual Job Taxonomies through Word Embeddings (Student Abstract)

Autor: Giabelli, A, Malandri, L, Mercorio, F, Mezzanzanica, M

Publikováno v: Proceedings of the AAAI Conference on Artificial Intelligence. 36:12955-12956

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d02e56d2f551991872c9949abbb7c11
https://doi.org/10.1609/aaai.v36i11.21614

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A Unified Framework for Intrinsic Evaluation of Word-Embedding Algorithms

Autor: Anna Giabelli

Publikováno v: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.

Word embeddings are widely used in copious Natural Language Processing tasks, including semantic analysis, information retrieval, dependency parsing, question answering, and machine translation. This extensive use implies that the evaluation of the p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0766878786e32a4bc37ebb0614c39210
https://doi.org/10.24963/ijcai.2022/829

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Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem

Autor: Anna Giabelli, Andrea Seveso, Fabio Mercorio, Lorenzo Malandri, Mario Mezzanzanica

Publikováno v: IJCAI

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 (t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ed769543724ac1aa6566ec3b3e86491
https://doi.org/10.24963/ijcai.2021/708

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