Ensemble Methods to Distinguish Mainland and Taiwan Chinese

Hai Hu, Wen Li, He Zhou, Zuoyu Tian, Yiwen Zhang, Liang Zou
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引用次数: 9

Abstract

This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a Naive Bayes classifier with word n-gram as features, SVMs with both character and syntactic features, and neural networks with pre-trained character/word embeddings. Then we adopt ensemble methods to combine output from base classifiers to make final predictions. Our ensemble models achieve the highest F1 score (0.893) in simplified Chinese track and the second highest (0.901) in traditional Chinese track. Our results demonstrate the effectiveness and robustness of the ensemble methods.
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大陆汉语与台湾汉语的集成方法辨析
本文描述了IUCL系统在VarDial 2019评估活动中用于区分大陆和台湾普通话变体的任务。我们首先构建了几个基本分类器,包括一个以单词n-gram为特征的朴素贝叶斯分类器,同时具有字符和句法特征的支持向量机,以及具有预训练字符/词嵌入的神经网络。然后采用集成方法将基分类器的输出组合起来进行最终预测。我们的集成模型在简体中文赛道上F1得分最高(0.893),在繁体中文赛道上F1得分第二高(0.901)。我们的结果证明了集成方法的有效性和鲁棒性。
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Joint Approach to Deromanization of Code-mixed Texts Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging TwistBytes - Identification of Cuneiform Languages and German Dialects at VarDial 2019 Ensemble Methods to Distinguish Mainland and Taiwan Chinese A Report on the Third VarDial Evaluation Campaign
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