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Semantic feature aggregation for gender identification in Russian facebook. / Panicheva, Polina; Mirzagitova, Aliia; Ledovaya, Yanina.

Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers. Vol. 789 Springer Nature, 2018. p. 3-15 (Communications in Computer and Information Science; Vol. 789).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Panicheva, P, Mirzagitova, A & Ledovaya, Y 2018, Semantic feature aggregation for gender identification in Russian facebook. in Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers. vol. 789, Communications in Computer and Information Science, vol. 789, Springer Nature, pp. 3-15, Conference on Artificial Intelligence and Natural Language, St. Petersburg, Russian Federation, 19/09/17. https://doi.org/10.1007/978-3-319-71746-3_1

APA

Panicheva, P., Mirzagitova, A., & Ledovaya, Y. (2018). Semantic feature aggregation for gender identification in Russian facebook. In Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers (Vol. 789, pp. 3-15). (Communications in Computer and Information Science; Vol. 789). Springer Nature. https://doi.org/10.1007/978-3-319-71746-3_1

Vancouver

Panicheva P, Mirzagitova A, Ledovaya Y. Semantic feature aggregation for gender identification in Russian facebook. In Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers. Vol. 789. Springer Nature. 2018. p. 3-15. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-71746-3_1

Author

Panicheva, Polina ; Mirzagitova, Aliia ; Ledovaya, Yanina. / Semantic feature aggregation for gender identification in Russian facebook. Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers. Vol. 789 Springer Nature, 2018. pp. 3-15 (Communications in Computer and Information Science).

BibTeX

@inproceedings{91987582e4a94504b23802444fc5ec1b,
title = "Semantic feature aggregation for gender identification in Russian facebook",
abstract = "The goal of the current work is to evaluate semantic feature aggregation techniques in a task of gender classification of public social media texts in Russian. We collect Facebook posts of Russian-speaking users and apply them as a dataset for two topic modelling techniques and a distributional clustering approach. The output of the algorithms is applied as a feature aggregation method in a task of gender classification based on a smaller Facebook sample. The classification performance of the best model is favorably compared against the lemmas baseline and the state-of-the-art results reported for a different genre or language. The resulting successful features are exemplified, and the difference between the three techniques in terms of classification performance and feature contents are discussed, with the best technique clearly outperforming the others.",
author = "Polina Panicheva and Aliia Mirzagitova and Yanina Ledovaya",
year = "2018",
doi = "10.1007/978-3-319-71746-3_1",
language = "English",
isbn = "9783319717456",
volume = "789",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "3--15",
booktitle = "Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers",
address = "Germany",
note = "Conference on Artificial Intelligence and Natural Language, AINL 2017 ; Conference date: 19-09-2017 Through 22-09-2017",
url = "http://ainlconf.ru/2017",

}

RIS

TY - GEN

T1 - Semantic feature aggregation for gender identification in Russian facebook

AU - Panicheva, Polina

AU - Mirzagitova, Aliia

AU - Ledovaya, Yanina

N1 - Conference code: 6

PY - 2018

Y1 - 2018

N2 - The goal of the current work is to evaluate semantic feature aggregation techniques in a task of gender classification of public social media texts in Russian. We collect Facebook posts of Russian-speaking users and apply them as a dataset for two topic modelling techniques and a distributional clustering approach. The output of the algorithms is applied as a feature aggregation method in a task of gender classification based on a smaller Facebook sample. The classification performance of the best model is favorably compared against the lemmas baseline and the state-of-the-art results reported for a different genre or language. The resulting successful features are exemplified, and the difference between the three techniques in terms of classification performance and feature contents are discussed, with the best technique clearly outperforming the others.

AB - The goal of the current work is to evaluate semantic feature aggregation techniques in a task of gender classification of public social media texts in Russian. We collect Facebook posts of Russian-speaking users and apply them as a dataset for two topic modelling techniques and a distributional clustering approach. The output of the algorithms is applied as a feature aggregation method in a task of gender classification based on a smaller Facebook sample. The classification performance of the best model is favorably compared against the lemmas baseline and the state-of-the-art results reported for a different genre or language. The resulting successful features are exemplified, and the difference between the three techniques in terms of classification performance and feature contents are discussed, with the best technique clearly outperforming the others.

UR - http://www.scopus.com/inward/record.url?scp=85037546120&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/semantic-feature-aggregation-gender-identification-russian-facebook

U2 - 10.1007/978-3-319-71746-3_1

DO - 10.1007/978-3-319-71746-3_1

M3 - Conference contribution

AN - SCOPUS:85037546120

SN - 9783319717456

VL - 789

T3 - Communications in Computer and Information Science

SP - 3

EP - 15

BT - Artificial Intelligence and Natural Language - 6th Conference, AINL 2017, Revised Selected Papers

PB - Springer Nature

T2 - Conference on Artificial Intelligence and Natural Language

Y2 - 19 September 2017 through 22 September 2017

ER -

ID: 13395534