Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Burcu Can"'
Publikováno v:
IEEE Access, Vol 11, Pp 56921-56934 (2023)
Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users’ emotions. This has numerous benefits, such as understanding how individuals feel about certain ne
Externí odkaz:
https://doaj.org/article/9c8b1304ac354c0e9af4e76d7d100d40
Autor:
Burcu Can, Suresh Manandhar
Publikováno v:
Computational Linguistics, Vol 44, Iss 2, Pp 349-374 (2018)
This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of
Externí odkaz:
https://doaj.org/article/175f8b45e5854742b8f1694bdd62f720
Publikováno v:
Natural Language Engineering. 28:763-795
We propose an integrated deep learning model for morphological segmentation, morpheme tagging, part-of-speech (POS) tagging, and syntactic parsing onto dependencies, using cross-level contextual information flow for every word, from segments to depen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::201a59cd99b45bc68e370f8184778c47
https://doi.org/10.1017/s1351324921000371
https://doi.org/10.1017/s1351324921000371
Autor:
Iqra Ameer, Necva Bölücü, Muhammad Hammad Fahim Siddiqui, Burcu Can, Grigori Sidorov, Alexander Gelbukh
Publikováno v:
Expert Systems with Applications. 213:118534
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17ac593262421f89c11341ef61417b3e
https://doi.org/10.1016/j.eswa.2022.118534
https://doi.org/10.1016/j.eswa.2022.118534
Publikováno v:
Expert Systems with Applications. 214:119103
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2edbc55e078ee9915db91781273a1bd3
https://doi.org/10.1016/j.eswa.2022.119103
https://doi.org/10.1016/j.eswa.2022.119103
Autor:
Burcu Can, Emre Kağan Akkaya
Publikováno v:
Natural Language Engineering. 27:35-64
In this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b600b84d3124f05df1f8e05a308327a
https://doi.org/10.1017/s1351324919000627
https://doi.org/10.1017/s1351324919000627
Publikováno v:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99fcd9b4087bbcc48ccd587a1564dab1
https://doi.org/10.18653/v1/2022.naacl-demo.3
https://doi.org/10.18653/v1/2022.naacl-demo.3
Publikováno v:
2021 International Conference on Asian Language Processing (IALP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b36b6ae85a9d4a5aad9c10835b614b3
https://doi.org/10.1109/ialp54817.2021.9675269
https://doi.org/10.1109/ialp54817.2021.9675269
Kültür varlıklarının korunması ve yönetilmesi süreçlerinde, koruma ile gelişme önerileri arasında denge kurulmasında yeni ve önemli bir araç olarak tanımlanan kültürel miras etki değerlendirme (KÜMED) konusu, 2000’li yılların
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3401::954efdf8563f4c3ef4a8a5e13cb32e34
https://hdl.handle.net/11729/3373
https://hdl.handle.net/11729/3373
Autor:
Burcu Can
Publikováno v:
Volume: 12, Issue: 3 183-193
Bilişim Teknolojileri Dergisi
Bilişim Teknolojileri Dergisi
Türkçe, morfem adı verilen birimlerin art arda eklenmesiyle sözcüklerinoluşturulduğu sondan eklemeli bir dildir. Sözcüklerin farklı parçalarınbirleştirilmesiyle oluşturulması makine tercümesi, duygu analizi ve bilgiçıkarımı gibi b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec0c8cef5a25ccb872c3db02cabee7c8
https://doi.org/10.17671/gazibtd.486042
https://doi.org/10.17671/gazibtd.486042