Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Martin Lentschat"'
Publikováno v:
Data in Brief, Vol 41, Iss , Pp 108000- (2022)
This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format
Externí odkaz:
https://doaj.org/article/9d016df819424fe5b1d792a66016b964
Publikováno v:
Data in Brief, Vol 36, Iss , Pp 107135- (2021)
This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the Scie
Externí odkaz:
https://doaj.org/article/86e583bfbd784bbf9b3f61c639b4102b
Publikováno v:
Expert Systems with Applications
A new method to extract knowledge structured as n-Ary relations from scientific articles is presented. We designed and assessed different approaches to reconstruct instances of n-Ary relations extracted from scientific articles in experimental domain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dfe99987d9e6940e79a6daacc9611e7
http://agritrop.cirad.fr/601747/
http://agritrop.cirad.fr/601747/
Autor:
Sonita Te, Amira Barhoumi, Martin Lentschat, Frédérique Bordignon, Cyril Labbé, François Portet
Publikováno v:
Scholarly Document Processing
Scholarly Document Processing, Oct 2022, Gyeongju, South Korea
HAL
Scholarly Document Processing, Oct 2022, Gyeongju, South Korea
HAL
International audience Recently, there have been numerous research in Natural Language Processing on citation analysis in scientific literature. Studies of citation behavior aim at finding how researchers cited a paper in their work. In this paper, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::461341e4600d6881e0d9d238b0853777
https://hal.science/hal-03820225
https://hal.science/hal-03820225
Publikováno v:
Data in Brief
This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01d1d3fde913519ba5d1bf5459024517
https://pubmed.ncbi.nlm.nih.gov/35295868
https://pubmed.ncbi.nlm.nih.gov/35295868
Publikováno v:
WIMS
WIMIS 2020: 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 20)
International conference on Web-Intelligence, Mining and Semantics
International conference on Web-Intelligence, Mining and Semantics, Jun 2020, Biarritz, France
WIMIS 2020: 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 20)
International conference on Web-Intelligence, Mining and Semantics
International conference on Web-Intelligence, Mining and Semantics, Jun 2020, Biarritz, France
Retrieving entities associated with experimental data in the textual content of scientific documents faces numbers of challenges. One of them is the assessment of the extracted entities for further process, especially the identification of false posi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62291335add6852860b5f8e9a198f83d
https://doi.org/10.1145/3405962.3405978
https://doi.org/10.1145/3405962.3405978
Publikováno v:
HAL
Spatial Analysis and Geomatics (SAGEO) 2021
Spatial Analysis and Geomatics (SAGEO) 2021, May 2021, La Rochelle, France
Spatial Analysis and Geomatics (SAGEO) 2021
Spatial Analysis and Geomatics (SAGEO) 2021, May 2021, La Rochelle, France
The Great National Debate, decided by Emmanuel Macron at the beginning of 2019 to respond to the Yellow Vests social movement, allowed the collection of citizens’ contributions on the ecological transition via an online platform. In this article, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e034bc7d0fa6ceaea9af3ea8bf6cc7ac
https://hal.archives-ouvertes.fr/hal-03375360
https://hal.archives-ouvertes.fr/hal-03375360