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E-hypertext Media Topic Model with Automatic Label Assignment. / Mitrofanova, Olga; Kriukova, Anna; Shulginov, Valery; Shulginov, Vadim.
Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings. ed. / Wil M. van der Aalst; Vladimir Batagelj; Alexey Buzmakov; Dmitry I. Ignatov; Anna Kalenkova; Michael Khachay; Olessia Koltsova; Andrey Kutuzov; Sergei O. Kuznetsov; Irina A. Lomazova; Natalia Loukachevitch; Ilya Makarov; Amedeo Napoli; Alexander Panchenko; Panos M. Pardalos; Marcello Pelillo; Andrey V. Savchenko; Elena Tutubalina. Springer Nature, 2021. p. 102-114 (Communications in Computer and Information Science; Vol. 1357 CCIS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Harvard
Mitrofanova, O, Kriukova, A, Shulginov, V & Shulginov, V 2021,
E-hypertext Media Topic Model with Automatic Label Assignment. in WM van der Aalst, V Batagelj, A Buzmakov, DI Ignatov, A Kalenkova, M Khachay, O Koltsova, A Kutuzov, SO Kuznetsov, IA Lomazova, N Loukachevitch, I Makarov, A Napoli, A Panchenko, PM Pardalos, M Pelillo, AV Savchenko & E Tutubalina (eds),
Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings. Communications in Computer and Information Science, vol. 1357 CCIS, Springer Nature, pp. 102-114, 9th International Conference on Analysis of Images, Social Networks, and Texts, AIST 2020, Virtual, Online,
15/10/20.
https://doi.org/10.1007/978-3-030-71214-3_9
APA
Mitrofanova, O., Kriukova, A., Shulginov, V., & Shulginov, V. (2021).
E-hypertext Media Topic Model with Automatic Label Assignment. In W. M. van der Aalst, V. Batagelj, A. Buzmakov, D. I. Ignatov, A. Kalenkova, M. Khachay, O. Koltsova, A. Kutuzov, S. O. Kuznetsov, I. A. Lomazova, N. Loukachevitch, I. Makarov, A. Napoli, A. Panchenko, P. M. Pardalos, M. Pelillo, A. V. Savchenko, & E. Tutubalina (Eds.),
Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings (pp. 102-114). (Communications in Computer and Information Science; Vol. 1357 CCIS). Springer Nature.
https://doi.org/10.1007/978-3-030-71214-3_9
Vancouver
Mitrofanova O, Kriukova A, Shulginov V, Shulginov V.
E-hypertext Media Topic Model with Automatic Label Assignment. In van der Aalst WM, Batagelj V, Buzmakov A, Ignatov DI, Kalenkova A, Khachay M, Koltsova O, Kutuzov A, Kuznetsov SO, Lomazova IA, Loukachevitch N, Makarov I, Napoli A, Panchenko A, Pardalos PM, Pelillo M, Savchenko AV, Tutubalina E, editors, Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings. Springer Nature. 2021. p. 102-114. (Communications in Computer and Information Science).
https://doi.org/10.1007/978-3-030-71214-3_9
Author
Mitrofanova, Olga ; Kriukova, Anna ; Shulginov, Valery ; Shulginov, Vadim. /
E-hypertext Media Topic Model with Automatic Label Assignment. Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings. editor / Wil M. van der Aalst ; Vladimir Batagelj ; Alexey Buzmakov ; Dmitry I. Ignatov ; Anna Kalenkova ; Michael Khachay ; Olessia Koltsova ; Andrey Kutuzov ; Sergei O. Kuznetsov ; Irina A. Lomazova ; Natalia Loukachevitch ; Ilya Makarov ; Amedeo Napoli ; Alexander Panchenko ; Panos M. Pardalos ; Marcello Pelillo ; Andrey V. Savchenko ; Elena Tutubalina. Springer Nature, 2021. pp. 102-114 (Communications in Computer and Information Science).
BibTeX
@inproceedings{a0c3427d9f274171940f73711b8cde7a,
title = "E-hypertext Media Topic Model with Automatic Label Assignment",
abstract = "This article deals with the principles of automatic label assignment for e-hypertext markup. We{\textquoteright}ve identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label{\textquoteright}s triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms{\textquoteright} results: at the first stage, two experts assessed the triplet{\textquoteright}s relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0» – a label doesn{\textquoteright}t match a topic; «1» – a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.",
keywords = "E-hypertext, Label assignment, Media discourse, Topic modelling",
author = "Olga Mitrofanova and Anna Kriukova and Valery Shulginov and Vadim Shulginov",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 9th International Conference on Analysis of Images, Social Networks, and Texts, AIST 2020 ; Conference date: 15-10-2020 Through 16-10-2020",
year = "2021",
doi = "10.1007/978-3-030-71214-3_9",
language = "English",
isbn = "9783030712136",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "102--114",
editor = "{van der Aalst}, {Wil M.} and Vladimir Batagelj and Alexey Buzmakov and Ignatov, {Dmitry I.} and Anna Kalenkova and Michael Khachay and Olessia Koltsova and Andrey Kutuzov and Kuznetsov, {Sergei O.} and Lomazova, {Irina A.} and Natalia Loukachevitch and Ilya Makarov and Amedeo Napoli and Alexander Panchenko and Pardalos, {Panos M.} and Marcello Pelillo and Savchenko, {Andrey V.} and Elena Tutubalina",
booktitle = "Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - E-hypertext Media Topic Model with Automatic Label Assignment
AU - Mitrofanova, Olga
AU - Kriukova, Anna
AU - Shulginov, Valery
AU - Shulginov, Vadim
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This article deals with the principles of automatic label assignment for e-hypertext markup. We’ve identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label’s triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms’ results: at the first stage, two experts assessed the triplet’s relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0» – a label doesn’t match a topic; «1» – a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.
AB - This article deals with the principles of automatic label assignment for e-hypertext markup. We’ve identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label’s triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms’ results: at the first stage, two experts assessed the triplet’s relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0» – a label doesn’t match a topic; «1» – a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.
KW - E-hypertext
KW - Label assignment
KW - Media discourse
KW - Topic modelling
UR - http://www.scopus.com/inward/record.url?scp=85107324185&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c4678b92-ce72-3d4f-85ce-20fd17fecc5c/
U2 - 10.1007/978-3-030-71214-3_9
DO - 10.1007/978-3-030-71214-3_9
M3 - Conference contribution
AN - SCOPUS:85107324185
SN - 9783030712136
T3 - Communications in Computer and Information Science
SP - 102
EP - 114
BT - Recent Trends in Analysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Supplementary Proceedings
A2 - van der Aalst, Wil M.
A2 - Batagelj, Vladimir
A2 - Buzmakov, Alexey
A2 - Ignatov, Dmitry I.
A2 - Kalenkova, Anna
A2 - Khachay, Michael
A2 - Koltsova, Olessia
A2 - Kutuzov, Andrey
A2 - Kuznetsov, Sergei O.
A2 - Lomazova, Irina A.
A2 - Loukachevitch, Natalia
A2 - Makarov, Ilya
A2 - Napoli, Amedeo
A2 - Panchenko, Alexander
A2 - Pardalos, Panos M.
A2 - Pelillo, Marcello
A2 - Savchenko, Andrey V.
A2 - Tutubalina, Elena
PB - Springer Nature
T2 - 9th International Conference on Analysis of Images, Social Networks, and Texts, AIST 2020
Y2 - 15 October 2020 through 16 October 2020
ER -
