Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Seung-Shik Kang"'
Publikováno v:
Digital Journalism. 9:84-105
This study aims to predict audience-rated news quality with journalistic values and linguistic/formal features of news articles, based on the theoretical rationales derived from information processing models, journalism and news consumption literatur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b9c6f5e443703dd160c5a5f72259971
https://doi.org/10.1080/21670811.2020.1842777
https://doi.org/10.1080/21670811.2020.1842777
Autor:
Hyun-Young Lee, Seung-Shik Kang
Publikováno v:
IEICE Transactions on Information and Systems. :2371-2378
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7df961f4a114975ae710f10a61e843ef
https://doi.org/10.1587/transinf.2020edp7038
https://doi.org/10.1587/transinf.2020edp7038
Publikováno v:
Journal of Information Processing Systems; Jun2022, Vol. 18 Issue 3, p344-358, 15p
Publikováno v:
SMA
While most embedding methods in the Korean language focus on morpheme unit to alleviate the out of vocabulary problem, recent researches in the English use the subword unit for embedding. Considering that a word is composed of subwords, which have a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::776464d10aa63ce6dbbca6da32286883
https://doi.org/10.1145/3426020.3426046
https://doi.org/10.1145/3426020.3426046
Publikováno v:
SMA
Representing a word into a continuous space, also known as a word vector, has been successful in various NLP tasks. The word-based embedding has two problems; one is the out-of-vocabulary problem and the other is does not take into account the contex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7f0561ff22493a07135b97a0c96b5b42
https://doi.org/10.1145/3426020.3426095
https://doi.org/10.1145/3426020.3426095
Autor:
Wonsup Jung, Seung-Shik Kang
Publikováno v:
ITA
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24994720b8025ddc61ac5ef515140237
https://doi.org/10.1109/ita50056.2020.9245006
https://doi.org/10.1109/ita50056.2020.9245006
Autor:
Yoon Gee Ong, Seung Shik Kang
Publikováno v:
Korean Institute of Smart Media. 7:54-59
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d410fe3c241563a2d1655ec056c34c9
https://doi.org/10.30693/smj.2018.7.2.54
https://doi.org/10.30693/smj.2018.7.2.54
Publikováno v:
Proceedings of the 2019 4th International Conference on Intelligent Information Technology.
Dependency parsing is the process of analyzing the linguistic relationship of words that make up a sentence. In natural language processing using deep learning, previously registered words are represented in a continuous vector space. However, if pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d16491b376cd3f1225affb449ee94980
https://doi.org/10.1145/3321454.3321469
https://doi.org/10.1145/3321454.3321469
Publikováno v:
Proceedings of the 2019 4th International Conference on Intelligent Information Technology.
In order to create a lyrics based on the characteristics of Korean, we reversed the K-pop lyrics data and use them as learning data. It transforms the incoming data to use certain elements of the sentence, such as predicates and conjunctions, as star
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16eb4c9530bb9e19d1a16d4e49c99904
https://doi.org/10.1145/3321454.3321470
https://doi.org/10.1145/3321454.3321470
Autor:
Seung-Shik Kang, Hyun-Young Lee
Publikováno v:
BigComp
SVM has been one of the most popular machine learning method for the binary classification such as sentiment analysis and spam message filtering. We explored a word embedding method for the construction of a feature vector and the deep learning metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a088d6d3f53c5dbc1b8254f71013d0c9
https://doi.org/10.1109/bigcomp.2019.8679476
https://doi.org/10.1109/bigcomp.2019.8679476