Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Andres Garcia-Silva"'
Autor:
Ian Roberts, Andres Garcia Silva, Cristian Berrìo Aroca, Jose Manuel Gómez-Pérez, Miroslav Jánoší, Dimitris Galanis, Rémi Calizzano, Andis Lagzdiņš, Milan Straka, Ulrich Germann
Publikováno v:
European Language Grid ISBN: 9783031172571
At the time of writing, the European Language Grid includes more than 800 LT services of varied types, including machine translation (MT), automatic speech recognition (ASR), text-to-speech synthesis (TTS), and text analysis ranging from simple token
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26c4fecc321d296c8ec47d6b62be34e2
https://doi.org/10.1007/978-3-031-17258-8_7
https://doi.org/10.1007/978-3-031-17258-8_7
This deliverable reports the progress in the design and development of the first version of the text mining and enrichment services for Research Objects and scientific documents in RELIANCE. In the early stage of the project, we gathered a corpus of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23306906236d2690c19190650a998d44
Autor:
Rosemarie Leone, Ilkay Altintas, Charles Meertens, Vito Romaniello, Stefano Salvi, Simone Mantovani, Cristiano Silvagni, Francesco De Leo, Federica Foglini, Jose Manuel Gomez-Perez, Sergio Albani, Melissa A. Genazzio, Henry W. Loescher, Raul Palma, Fran Boler, Mirko Albani, Fulvio Marelli, Michele Lazzarini, Andres Garcia-Silva, Marcin Krystek, Timothy Aldridge, Valentina Grande, Daniel Crawl, Elisa Trasatti, Helen Glaves, Hazel J. Napier, Christine Laney
Publikováno v:
Future generation computer systems 98 (2019): 550–564. doi:10.1016/j.future.2019.03.046
info:cnr-pdr/source/autori:Garcia-Silva A.; Gomez-Perez J.M.; Palma R.; Krystek M.; Mantovani S.; Foglini F.; Grande V.; De Leo F.; Salvi S.; Trasatti E.; Romaniello V.; Albani M.; Silvagni C.; Leone R.; Marelli F.; Albani S.; Lazzarini M.; Napier H.J.; Glaves H.M.; Aldridge T.; Meertens C.; Boler F.; Loescher H.W.; Laney C.; Genazzio M.A.; Crawl D.; Altintas I./titolo:Enabling FAIR research in Earth Science through research objects/doi:10.1016%2Fj.future.2019.03.046/rivista:Future generation computer systems/anno:2019/pagina_da:550/pagina_a:564/intervallo_pagine:550–564/volume:98
info:cnr-pdr/source/autori:Garcia-Silva A.; Gomez-Perez J.M.; Palma R.; Krystek M.; Mantovani S.; Foglini F.; Grande V.; De Leo F.; Salvi S.; Trasatti E.; Romaniello V.; Albani M.; Silvagni C.; Leone R.; Marelli F.; Albani S.; Lazzarini M.; Napier H.J.; Glaves H.M.; Aldridge T.; Meertens C.; Boler F.; Loescher H.W.; Laney C.; Genazzio M.A.; Crawl D.; Altintas I./titolo:Enabling FAIR research in Earth Science through research objects/doi:10.1016%2Fj.future.2019.03.046/rivista:Future generation computer systems/anno:2019/pagina_da:550/pagina_a:564/intervallo_pagine:550–564/volume:98
Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific information and reso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52ea6dde929a9d7641dac2478d4d123a
https://doi.org/10.1016/j.future.2019.03.046
https://doi.org/10.1016/j.future.2019.03.046
In essence, embedding algorithms work by optimizing the distance between a word and its usual context in order to generate an embedding space that encodes the distributional representation of words. In addition to single words or word pieces, other f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84d07dc217ebb542d7695169a103b973
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030721121
ECIR (1)
ECIR (1)
We investigate the self-attention mechanism of BERT in a fine-tuning scenario for the classification of scientific articles over a taxonomy of research disciplines. We observe how self-attention focuses on words that are highly related to the domain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ea2237e8b14e52003de2817b6c925a0
https://doi.org/10.1007/978-3-030-72113-8_11
https://doi.org/10.1007/978-3-030-72113-8_11
Publikováno v:
A Practical Guide to Hybrid Natural Language Processing ISBN: 9783030448295
Early word embeddings algorithms like word2vec and GloVe generate static distributional representations for words regardless of the context and the sense in which the word is used in a given sentence, offering poor modeling of ambiguous words and lac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d94c1d82b7ee4e6d662eea39590e1875
https://doi.org/10.1007/978-3-030-44830-1_3
https://doi.org/10.1007/978-3-030-44830-1_3
Publikováno v:
A Practical Guide to Hybrid Natural Language Processing ISBN: 9783030448295
In previous chapters we have seen a variety of ways to train models to derive embedding spaces for words and concepts and other nodes in knowledge graphs. As you often do not have control over the full training procedure, you may find yourself with s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6db2167111d643bceccc37a7c9d9aae4
https://doi.org/10.1007/978-3-030-44830-1_9
https://doi.org/10.1007/978-3-030-44830-1_9
Publikováno v:
A Practical Guide to Hybrid Natural Language Processing ISBN: 9783030448295
The proliferation of knowledge graphs and recent advances in artificial intelligence have raised great expectations related to the combination of symbolic and data-driven approaches in cognitive tasks. This is particularly the case of knowledge-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4e5def56aa99ba1147d767d962eadb1
https://doi.org/10.1007/978-3-030-44830-1_1
https://doi.org/10.1007/978-3-030-44830-1_1
Publikováno v:
A Practical Guide to Hybrid Natural Language Processing ISBN: 9783030448295
In this chapter we focus on knowledge graph embeddings, an approach to produce embeddings for concepts and names that are the main nodes in knowledge graphs, as well as the relations between them. The resulting embeddings aim to capture the knowledge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::052845e5525fedba048e7524986b6cc6
https://doi.org/10.1007/978-3-030-44830-1_5
https://doi.org/10.1007/978-3-030-44830-1_5
Publikováno v:
A Practical Guide to Hybrid Natural Language Processing ISBN: 9783030448295
Disinformation and fake news are complex and important problems where natural language processing can play an important role in helping people navigate online content. In this chapter, we provide various practical tutorials where we apply several of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::188321dd142b888e145f3ecf6e20c03d
https://doi.org/10.1007/978-3-030-44830-1_10
https://doi.org/10.1007/978-3-030-44830-1_10