Standard
Stable topic modeling with local density regularization. / Koltcov, Sergei; Nikolenko, Sergey I.; Koltsova, Olessia; Filippov, Vladimir; Bodrunova, Svetlana.
Internet Science - 3rd International Conference, INSCI 2016, Proceedings. ред. / Anna Satsiou; Yanina Welp; Thanassis Tiropanis; Dominic DiFranzo; Ioannis Stavrakakis; Franco Bagnoli; Paolo Nesi; Giovanna Pacini. Springer Nature, 2016. стр. 176-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 9934 LNCS).
Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Harvard
Koltcov, S
, Nikolenko, SI, Koltsova, O, Filippov, V
& Bodrunova, S 2016,
Stable topic modeling with local density regularization. в A Satsiou, Y Welp, T Tiropanis, D DiFranzo, I Stavrakakis, F Bagnoli, P Nesi & G Pacini (ред.),
Internet Science - 3rd International Conference, INSCI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 9934 LNCS, Springer Nature, стр. 176-188, 3rd International Conference on Internet Science, INSCI 2016, Florence, Италия,
12/09/16.
https://doi.org/10.1007/978-3-319-45982-016APA
Koltcov, S.
, Nikolenko, S. I., Koltsova, O., Filippov, V.
, & Bodrunova, S. (2016).
Stable topic modeling with local density regularization. в A. Satsiou, Y. Welp, T. Tiropanis, D. DiFranzo, I. Stavrakakis, F. Bagnoli, P. Nesi, & G. Pacini (Ред.),
Internet Science - 3rd International Conference, INSCI 2016, Proceedings (стр. 176-188). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 9934 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-319-45982-016Vancouver
Koltcov S
, Nikolenko SI, Koltsova O, Filippov V
, Bodrunova S.
Stable topic modeling with local density regularization. в Satsiou A, Welp Y, Tiropanis T, DiFranzo D, Stavrakakis I, Bagnoli F, Nesi P, Pacini G, Редакторы, Internet Science - 3rd International Conference, INSCI 2016, Proceedings. Springer Nature. 2016. стр. 176-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-319-45982-016Author
Koltcov, Sergei
; Nikolenko, Sergey I. ; Koltsova, Olessia ; Filippov, Vladimir
; Bodrunova, Svetlana. /
Stable topic modeling with local density regularization. Internet Science - 3rd International Conference, INSCI 2016, Proceedings. Редактор / Anna Satsiou ; Yanina Welp ; Thanassis Tiropanis ; Dominic DiFranzo ; Ioannis Stavrakakis ; Franco Bagnoli ; Paolo Nesi ; Giovanna Pacini. Springer Nature, 2016. стр. 176-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{c1ca67b55b284f06b6f67324921465c6,
title = "Stable topic modeling with local density regularization",
abstract = "Topic modeling has emerged over the last decade as a powerful tool for analyzing large text corpora, including Web-based usergenerated texts. Topic stability, however, remains a concern: topic models have a very complex optimization landscape with many local maxima, and even different runs of the same model yield very different topics. Aiming to add stability to topic modeling, we propose an approach to topic modeling based on local density regularization, where words in a local context window of a given word have higher probabilities to get the same topic as that word. We compare several models with local density regularizers and show how they can improve topic stability while remaining on par with classical models in terms of quality metrics.",
keywords = "Gibbs sampling, Latent Dirichlet allocation, Topic modeling",
author = "Sergei Koltcov and Nikolenko, {Sergey I.} and Olessia Koltsova and Vladimir Filippov and Svetlana Bodrunova",
note = "Koltcov S., Nikolenko S.I., Koltsova O., Filippov V., Bodrunova S. (2016) Stable Topic Modeling with Local Density Regularization. In: Bagnoli F. et al. (eds) Internet Science. INSCI 2016. Lecture Notes in Computer Science, vol 9934. Springer, Cham. https://doi.org/10.1007/978-3-319-45982-0_16; 3rd International Conference on Internet Science, INSCI 2016 ; Conference date: 12-09-2016 Through 14-09-2016",
year = "2016",
doi = "10.1007/978-3-319-45982-016",
language = "English",
isbn = "9783319459813",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "176--188",
editor = "Anna Satsiou and Yanina Welp and Thanassis Tiropanis and Dominic DiFranzo and Ioannis Stavrakakis and Franco Bagnoli and Paolo Nesi and Giovanna Pacini",
booktitle = "Internet Science - 3rd International Conference, INSCI 2016, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Stable topic modeling with local density regularization
AU - Koltcov, Sergei
AU - Nikolenko, Sergey I.
AU - Koltsova, Olessia
AU - Filippov, Vladimir
AU - Bodrunova, Svetlana
N1 - Koltcov S., Nikolenko S.I., Koltsova O., Filippov V., Bodrunova S. (2016) Stable Topic Modeling with Local Density Regularization. In: Bagnoli F. et al. (eds) Internet Science. INSCI 2016. Lecture Notes in Computer Science, vol 9934. Springer, Cham. https://doi.org/10.1007/978-3-319-45982-0_16
PY - 2016
Y1 - 2016
N2 - Topic modeling has emerged over the last decade as a powerful tool for analyzing large text corpora, including Web-based usergenerated texts. Topic stability, however, remains a concern: topic models have a very complex optimization landscape with many local maxima, and even different runs of the same model yield very different topics. Aiming to add stability to topic modeling, we propose an approach to topic modeling based on local density regularization, where words in a local context window of a given word have higher probabilities to get the same topic as that word. We compare several models with local density regularizers and show how they can improve topic stability while remaining on par with classical models in terms of quality metrics.
AB - Topic modeling has emerged over the last decade as a powerful tool for analyzing large text corpora, including Web-based usergenerated texts. Topic stability, however, remains a concern: topic models have a very complex optimization landscape with many local maxima, and even different runs of the same model yield very different topics. Aiming to add stability to topic modeling, we propose an approach to topic modeling based on local density regularization, where words in a local context window of a given word have higher probabilities to get the same topic as that word. We compare several models with local density regularizers and show how they can improve topic stability while remaining on par with classical models in terms of quality metrics.
KW - Gibbs sampling
KW - Latent Dirichlet allocation
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=84986237205&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-45982-016
DO - 10.1007/978-3-319-45982-016
M3 - Conference contribution
SN - 9783319459813
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 176
EP - 188
BT - Internet Science - 3rd International Conference, INSCI 2016, Proceedings
A2 - Satsiou, Anna
A2 - Welp, Yanina
A2 - Tiropanis, Thanassis
A2 - DiFranzo, Dominic
A2 - Stavrakakis, Ioannis
A2 - Bagnoli, Franco
A2 - Nesi, Paolo
A2 - Pacini, Giovanna
PB - Springer Nature
T2 - 3rd International Conference on Internet Science, INSCI 2016
Y2 - 12 September 2016 through 14 September 2016
ER -
