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A hybrid approach for anaphora resolution in the Russian language. / Kozlova, Anna; Svischev, Alexey; Gureenkova, Olga et al.

Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 36-40 8071960.

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

Harvard

Kozlova, A, Svischev, A, Gureenkova, O & Batura, T 2017, A hybrid approach for anaphora resolution in the Russian language. in Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017., 8071960, Institute of Electrical and Electronics Engineers Inc., pp. 36-40, 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017, Novosibirsk, Akademgorodok, Russian Federation, 12.04.2017. https://doi.org/10.1109/SSDSE.2017.8071960

APA

Kozlova, A., Svischev, A., Gureenkova, O., & Batura, T. (2017). A hybrid approach for anaphora resolution in the Russian language. In Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 (pp. 36-40). [8071960] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSDSE.2017.8071960

Vancouver

Kozlova A, Svischev A, Gureenkova O, Batura T. A hybrid approach for anaphora resolution in the Russian language. In Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 36-40. 8071960 doi: 10.1109/SSDSE.2017.8071960

Author

Kozlova, Anna ; Svischev, Alexey ; Gureenkova, Olga et al. / A hybrid approach for anaphora resolution in the Russian language. Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 36-40

BibTeX

@inproceedings{55e1455edfa54ef59e9bccab2640ca02,
title = "A hybrid approach for anaphora resolution in the Russian language",
abstract = "The paper is dedicated to applying a hybrid approach based on rules and machine learning for anaphora resolution in the Russian language. The model combines formal rules, the Extra Trees machine learning algorithm and the Balance Cascade algorithm for working with imbalanced learning sets. A number of features were obtained from the rules or were generated from other features; in addition, the syntactic context was taken into account. A neural network algorithm SyntaxNet was used to analyze the syntactic context.",
keywords = "anaphora, antecedent, classification, coreference, ensemble learning, machine learning",
author = "Anna Kozlova and Alexey Svischev and Olga Gureenkova and Tatiana Batura",
year = "2017",
month = oct,
day = "18",
doi = "10.1109/SSDSE.2017.8071960",
language = "English",
pages = "36--40",
booktitle = "Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 ; Conference date: 12-04-2017 Through 13-04-2017",

}

RIS

TY - GEN

T1 - A hybrid approach for anaphora resolution in the Russian language

AU - Kozlova, Anna

AU - Svischev, Alexey

AU - Gureenkova, Olga

AU - Batura, Tatiana

PY - 2017/10/18

Y1 - 2017/10/18

N2 - The paper is dedicated to applying a hybrid approach based on rules and machine learning for anaphora resolution in the Russian language. The model combines formal rules, the Extra Trees machine learning algorithm and the Balance Cascade algorithm for working with imbalanced learning sets. A number of features were obtained from the rules or were generated from other features; in addition, the syntactic context was taken into account. A neural network algorithm SyntaxNet was used to analyze the syntactic context.

AB - The paper is dedicated to applying a hybrid approach based on rules and machine learning for anaphora resolution in the Russian language. The model combines formal rules, the Extra Trees machine learning algorithm and the Balance Cascade algorithm for working with imbalanced learning sets. A number of features were obtained from the rules or were generated from other features; in addition, the syntactic context was taken into account. A neural network algorithm SyntaxNet was used to analyze the syntactic context.

KW - anaphora

KW - antecedent

KW - classification

KW - coreference

KW - ensemble learning

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85040323402&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=34979538

U2 - 10.1109/SSDSE.2017.8071960

DO - 10.1109/SSDSE.2017.8071960

M3 - Conference contribution

AN - SCOPUS:85040323402

SP - 36

EP - 40

BT - Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017

Y2 - 12 April 2017 through 13 April 2017

ER -

ID: 9094337