A Cross-Lingual Approach for Building Multilingual Sentiment Lexicons

被引：1

作者：

Naderalvojoud, Behzad ^{[1
]}

Qasemizadeh, Behrang ^{[2
]}

Kallmeyer, Laura ^{[2
]}

Sezer, Ebru Akcapinar ^{[1
]}

机构：

[1] Hacettepe Univ, TR-06800 Ankara, Turkey

[2] Univ Dusseldorf, DFG SFB 991, Dusseldorf, Germany

来源：

TEXT, SPEECH, AND DIALOGUE (TSD 2018) | 2018年 / 11107卷

关键词：

D O I：

10.1007/978-3-030-00794-2_28

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a cross-lingual distributional model to build sentiment lexicons in many languages from resources available in English. We evaluate this method for two languages, German and Turkish, and on several datasets. We show that the sentiment lexicons built using our method remarkably improve the performance of a state-of-the-art lexicon-based BiLSTM sentiment classifier.

引用

页码：259 / 266

页数：8

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