2019共享任务:阿拉伯语细粒度方言识别

WANLP@ACL 2019 Pub Date : 2019-08-01 DOI:10.18653/v1/W19-4635

Mourad Abbas, Mohamed Lichouri, Abed Alhakim Freihat

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引用次数: 9

摘要

本文描述了我们针对MADAR 2019阿拉伯语细粒度方言识别任务提出的解决方案。提出的解决方案利用了一组我们在字符和单词特征上训练的分类器。这些分类器是:支持向量机(SVM)，伯努利朴素贝叶斯(BNB)，多项朴素贝叶斯(MNB)，逻辑回归(LR)，随机梯度下降(SGD)，被动攻击(PA)和感知器(PC)。该系统取得了较好的效果，开发集和测试集的性能分别为62.87%和62.12%。

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ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 % and 62.12 % for both development and test sets.

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WANLP@ACL 2019

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