Zobrazeno 1 - 10
of 287
pro vyhledávání: '"Mezzanzanica, M."'
Publikováno v:
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE).
The growing number of machine learning-based Cyber-Physical Systems (CPSs) and their ability to adapt and to learn is gaining research interest in several biomedical applications. The use of learning capabilities allows CPSs to interact and analyse t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37718e843064fc875a39844afa26b209
https://doi.org/10.1109/metroxraine54828.2022.9967673
https://doi.org/10.1109/metroxraine54828.2022.9967673
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
https://doi.org/10.1609/aaai.v35i18.18004
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
https://doi.org/10.1609/aaai.v36i11.21614
Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an”human-in-the-loop” semi-supe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::2e4fcb77e26b8d284d16bb31d0ac606c
http://hdl.handle.net/10281/362351
http://hdl.handle.net/10281/362351
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 (Unit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::e04d3f9d508b7ac446289c361cd83ed3
http://hdl.handle.net/10281/385830
http://hdl.handle.net/10281/385830
Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::c47dec56e16b4cc88e08c649fc2dbab0
http://hdl.handle.net/10281/362349
http://hdl.handle.net/10281/362349
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 sta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::5b0683e74b79ad446f1ef10135beaa48
http://hdl.handle.net/10281/385828
http://hdl.handle.net/10281/385828
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Akademický článek
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