Standard

Complex approach towards algoritm learning for anaphora resolution in Russian language. / Gureenkova, O. A.; Batura, T. V.; Kozlova, A. A. et al.

In: Komp'juternaja Lingvistika i Intellektual'nye Tehnologii, Vol. 1, No. 16, 2017, p. 89-97.

Research output: Contribution to journal › Article › peer-review

Harvard

Gureenkova, OA, Batura, TV, Kozlova, AA & Svischev, AN 2017, 'Complex approach towards algoritm learning for anaphora resolution in Russian language', Komp'juternaja Lingvistika i Intellektual'nye Tehnologii, vol. 1, no. 16, pp. 89-97.

APA

Gureenkova, O. A., Batura, T. V., Kozlova, A. A., & Svischev, A. N. (2017). Complex approach towards algoritm learning for anaphora resolution in Russian language. Komp'juternaja Lingvistika i Intellektual'nye Tehnologii, 1(16), 89-97.

Vancouver

Gureenkova OA, Batura TV, Kozlova AA, Svischev AN. Complex approach towards algoritm learning for anaphora resolution in Russian language. Komp'juternaja Lingvistika i Intellektual'nye Tehnologii. 2017;1(16):89-97.

Author

Gureenkova, O. A. ; Batura, T. V. ; Kozlova, A. A. et al. / Complex approach towards algoritm learning for anaphora resolution in Russian language. In: Komp'juternaja Lingvistika i Intellektual'nye Tehnologii. 2017 ; Vol. 1, No. 16. pp. 89-97.

BibTeX

@article{41cbe4f9bfaf4d668c29e02337da80d6,
title = "Complex approach towards algoritm learning for anaphora resolution in Russian language",
abstract = "The paper considers applying of ensemble algorithm based on rules and machine learning for anaphora resolution in Russian language. Ensemble presents combination of formal rules, a machine learning algorithm Extra Trees and an algorithm for working with imbalanced learning sets Balance Cascade. Complexity of the approach lies in generation of complex features from rules and vectorization of syntactic context, with context data obtained from algorithms mystem (Yandex), SyntaxNet (Google) and Word2Vec.",
keywords = "Anaphora, Antecedent, Balance Cascade, Cataphora, Extra Trees, Imbalanced set, Machine learning, Random forest, SyntaxNet, Word2Vec",
author = "Gureenkova, {O. A.} and Batura, {T. V.} and Kozlova, {A. A.} and Svischev, {A. N.}",
year = "2017",
language = "English",
volume = "1",
pages = "89--97",
journal = "Компьютерная лингвистика и интеллектуальные технологии",
issn = "2221-7932",
publisher = "Komp'juternaja Lingvistika i Intellektual'nye Tehnologii",
number = "16",

}

RIS

TY - JOUR

T1 - Complex approach towards algoritm learning for anaphora resolution in Russian language

AU - Gureenkova, O. A.

AU - Batura, T. V.

AU - Kozlova, A. A.

AU - Svischev, A. N.

PY - 2017

Y1 - 2017

N2 - The paper considers applying of ensemble algorithm based on rules and machine learning for anaphora resolution in Russian language. Ensemble presents combination of formal rules, a machine learning algorithm Extra Trees and an algorithm for working with imbalanced learning sets Balance Cascade. Complexity of the approach lies in generation of complex features from rules and vectorization of syntactic context, with context data obtained from algorithms mystem (Yandex), SyntaxNet (Google) and Word2Vec.

AB - The paper considers applying of ensemble algorithm based on rules and machine learning for anaphora resolution in Russian language. Ensemble presents combination of formal rules, a machine learning algorithm Extra Trees and an algorithm for working with imbalanced learning sets Balance Cascade. Complexity of the approach lies in generation of complex features from rules and vectorization of syntactic context, with context data obtained from algorithms mystem (Yandex), SyntaxNet (Google) and Word2Vec.

KW - Anaphora

KW - Antecedent

KW - Balance Cascade

KW - Cataphora

KW - Extra Trees

KW - Imbalanced set

KW - Machine learning

KW - Random forest

KW - SyntaxNet

KW - Word2Vec

UR - http://www.scopus.com/inward/record.url?scp=85021792831&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85021792831

VL - 1

SP - 89

EP - 97

JO - Компьютерная лингвистика и интеллектуальные технологии

JF - Компьютерная лингвистика и интеллектуальные технологии

SN - 2221-7932

IS - 16

ER -

ID: 9076636