Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Mirko Bronzi"'
Publikováno v:
ICASSP
Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parallelization of their computations. Transformers are emerging as a natura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faf61384e07315e17cc8acf749652e92
Publikováno v:
Scopus-Elsevier
We present an unsupervised approach for harvesting the data exposed by a set of structured and partially overlapping data-intensive web sources. Our proposal comes within a formal framework tackling two problems: the data extraction problem, to gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7fa27fd9dd9e15a200605ffb3e98cb0
https://doi.org/10.14778/2536206.2536209
https://doi.org/10.14778/2536206.2536209
Publikováno v:
WebDB
A large number of web sites publish pages containing structured information about recognizable concepts, but these data are only partially used by current applications. Although such information is spread across a myriad of sources, the web scale imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2c3f59627fdeff15ab3ef87c486a973
https://doi.org/10.1145/1859127.1859137
https://doi.org/10.1145/1859127.1859137
Publikováno v:
WWW
A large number of web sites publish pages containing structured information about recognizable concepts, but these data are only partially used by current applications. Although such information is spread across a myriad of sources, the web scale imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6ea311b7479509a9515669de72643fe
https://hdl.handle.net/11590/177411
https://hdl.handle.net/11590/177411
Publikováno v:
Web Intelligence
Exploiting the huge amount of data available on the Web involves the generation of wrappers to extract data from web pages. We argue that existing approaches for web data extraction from data-intensive websites miss the opportunities related to the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e14a0535bd3b7eeb6f5733c200dcb50
https://doi.org/10.1109/wi-iat.2011.160
https://doi.org/10.1109/wi-iat.2011.160
Publikováno v:
WWW (Companion Volume)
A relevant number of web sites publish structured data about recognizable concepts (such as stock quotes, movies, restau- rants, etc.). There is a great chance to create applications that rely on a huge amount of data taken from the Web. We present a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a339887271f47dc4d039270d375e03e
https://hdl.handle.net/11590/186181
https://hdl.handle.net/11590/186181