A hybrid approach for anaphora resolution in the Russian language

Anna Kozlova; Alexey Svischev; Olga Gureenkova; Tatiana Batura

doi:10.1109/SSDSE.2017.8071960

A hybrid approach for anaphora resolution in the Russian language

Anna Kozlova, Alexey Svischev, Olga Gureenkova, Tatiana Batura

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › рецензирование

4 Цитирования (Scopus)

Аннотация

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.

Язык оригинала	английский
Название основной публикации	Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017
Издатель	Institute of Electrical and Electronics Engineers Inc.
Страницы	36-40
Число страниц	5
ISBN (электронное издание)	9781538615935
DOI	https://doi.org/10.1109/SSDSE.2017.8071960
Состояние	Опубликовано - 18 окт 2017
Событие	2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 - Novosibirsk, Akademgorodok, Российская Федерация Продолжительность: 12 апр 2017 → 13 апр 2017

Конференция

Конференция	2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017
Страна	Российская Федерация
Город	Novosibirsk, Akademgorodok
Период	12.04.2017 → 13.04.2017

Доступ к документу

10.1109/SSDSE.2017.8071960

Link to publication in Scopus

Fingerprint Подробные сведения о темах исследования «A hybrid approach for anaphora resolution in the Russian language». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать

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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",

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doi = "10.1109/SSDSE.2017.8071960",

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booktitle = "Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017",

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note = "2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 ; Conference date: 12-04-2017 Through 13-04-2017",

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

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

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

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › рецензирование

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