Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ozdowska, Sylwia"'
Autor:
Ghor, Tayeb Abderrahmani, Agrawal, Esha, Alam, Mehwish, Alqawasmeh, Omar, D'amato, Claudia, Annane, Amina, Azzam, Amr, Berezovskyi, Andrew, Biswas, Russa, Bonduel, Mathias, Brabant, Quentin, Bucur, Cristina-iulia, Camossi, Elena, Carriero, Valentina Anita, Chari, Shruthi, Fraga, David Chaves, Ciroku, Fiorela, Cochez, Michael, Curien, Hubert, Cutrona, Vincenzo, Dandan, Rahma, Dess, Danilo, Di Carlo, Valerio, Djebri, Ahmed El Amine, Van Erp, Marieke, Falakh, Faiq Miftakhul, Izquierdo, Alba Fernndez, Futia, Giuseppe, Gangemi, Aldo, Gasperoni, Simone, Grall, Arnaud, Heling, Lars, Henri, Pierre, Herradi, Noura, Issa, Subhi, Jozashoori, Samaneh, Juniarta, Nyoman, Kaffee, Lucie-aime, Keles, Ilkcan, Khare, Prashant, Kovtun, Viktor, Leone, Valentina, Li, Siying, Lieber, Sven, Lisena, Pasquale, Makhalova, Tatiana, Marinucci, Ludovica, Minier, Thomas, Moreau, Benjamin, Loustaunau, Alberto Moya, Nandini, Durgesh, Ozdowska, Sylwia, De Moura, Amanda Pacini, Padhee, Swati, Palma, Guillermo, Jimnez, Pedro Del Pozo, Presutti, Valentina, Reda, Roberto, Rizza, Ettore, Rosales-mndez, Henry, Rudolph, Sebastian, Sack, Harald, Sciullo, Luca, Simanjuntak, Humasak, Stomeo, Carlo, Thanapalasingam, Thiviyan, Tietz, Tabea, Varanka, Dalia, Vidal, Maria-esther, Wolowyk, Michael, Zocholl, Maximilian
Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want
Externí odkaz:
http://arxiv.org/abs/1903.12554
Autor:
Ozdowska, Sylwia, Claveau, Vincent
Publikováno v:
7th Language Resources and Evaluation Conference, LREC'10
7th Language Resources and Evaluation Conference, LREC'10, ELRA, May 2010, Valletta, Malta
7th Language Resources and Evaluation Conference, LREC'10, ELRA, May 2010, Valletta, Malta
International audience; This paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the source and target texts and is based on a machine-learning technique,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c79731cbe4aa0564ac691f87354f26a5
https://inria.hal.science/inria-00561754
https://inria.hal.science/inria-00561754
Autor:
Ozdowska, Sylwia
In this paper, we argue about the quality of bilingual data for statistical machine translation in terms of the original source language and translation direction in the context of a French to English translation task. We show that data containing or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______119::12d3bbb04f2fe47129899aac173fe9fb
http://doras.dcu.ie/15171/1/Ozdowska_taln_09.pdf
http://doras.dcu.ie/15171/1/Ozdowska_taln_09.pdf
In most statistical machine translation (SMT) systems, bilingual segments are extracted via word alignment. In this paper we compare alignments tuned directly according to alignment F-score and BLEU score in order to investigate the alignment charact
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______119::b2dcc68ccd6dec64973826e27b4b8af9
http://doras.dcu.ie/15161/1/LambertEtAl_mts_09.pdf
http://doras.dcu.ie/15161/1/LambertEtAl_mts_09.pdf
We consider the value of replacing and/or combining string-based methods with syntax-based methods for phrase-based statistical machine translation (PBSMT), and we also consider the relative merits of using constituency-annotated vs. dependency-annot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______119::5a7af5fb89123e55a8d2719328925275
http://doras.dcu.ie/15193/1/HearneEtAl_taln_08.pdf
http://doras.dcu.ie/15193/1/HearneEtAl_taln_08.pdf