A hybrid approach for anaphora resolution in the Russian language

被引：0
|
作者：
Kozlova, Anna [1 ]
Svischev, Alexey [1 ]
Gureenkova, Olga [2 ]
Batura, Tatiana [1 ,3 ]
机构：
[1] Novosibirsk State Univ, Novosibirsk, Russia
[2] Expasoft Ltd, Novosibirsk, Russia
[3] AP Ershov Inst Informat Syst SB RAS, Novosibirsk, Russia
来源：
2017 SIBERIAN SYMPOSIUM ON DATA SCIENCE AND ENGINEERING (SSDSE) | 2017年
关键词：
anaphora; antecedent; machine learning; coreference; classification; ensemble learning;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：36 / 40
页数：5
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