Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Wael Farhan"'
Autor:
Wael Farhan, Muhy Eddin Za'Ter, Qusai Abu Obaidah, Hisham Al Bataineh, Zyad Sober, Hussein Al Natsheh
Publikováno v:
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 28, Iss 1, Pp 103-110 (2021)
This paper proposes a novel approach to an automatic estimation of three speaker traits from Arabic speech: gender, emotion, and dialect. After showing promising results on different text classification tasks, the multi-task learning (MTL) approach i
Externí odkaz:
https://doaj.org/article/83418697d1d4489cbd64f9e253009397
Autor:
Muhy Eddin Za'ter, Hussein T. Al-Natsheh, Hisham al Bataineh, Zyad Sober, Wael Farhan, Qusai Abu Obaidah
Publikováno v:
FRUCT
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 28, Iss 1, Pp 103-110 (2021)
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 28, Iss 1, Pp 103-110 (2021)
This paper proposes a novel approach to an automatic estimation of three speaker traits from Arabic speech: gender, emotion, and dialect. After showing promising results on different text classification tasks, the multi-task learning (MTL) approach i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e78c127cc1477defca47fa309fe09e3
Publikováno v:
ICTAI
Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92221b7f6123c0a5b455b4329682c175
https://doi.org/10.1109/ictai.2019.00229
https://doi.org/10.1109/ictai.2019.00229
Publikováno v:
WANLP@ACL 2019
Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb1325c7cb83b67d6c70255549c4077d
https://doi.org/10.18653/v1/w19-4629
https://doi.org/10.18653/v1/w19-4629
Autor:
Ahmad Bisher Tarakji, Ruba Waleed Jaikat, Wael Farhan, Bashar Talafha, Mahmoud Al-Ayyoub, Anas Toma, Analle Abuammar
Publikováno v:
Information Processing & Management. 57:102181
In this paper, we present the first work on unsupervised dialectal Neural Machine Translation (NMT), where the source dialect is not represented in the parallel training corpus. Two systems are proposed for this problem. The first one is the Dialecta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e10f4e640ae75d9efc39c062d78dffa8
https://doi.org/10.1016/j.ipm.2019.102181
https://doi.org/10.1016/j.ipm.2019.102181
Publikováno v:
JMIR Medical Informatics
Background: Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). Objective: Our wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc6cfdd53f28e9c2d1f90b81431eb89d
https://doi.org/10.2196/medinform.5977
https://doi.org/10.2196/medinform.5977
Publikováno v:
Clinical Neurophysiology. 122:S162-S163
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faac617c3278f3dfd5c7931ff31d7c98
https://doi.org/10.1016/s1388-2457(11)60584-1
https://doi.org/10.1016/s1388-2457(11)60584-1