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Compression versus traditional machine learning classifiers to detect code-switching in varieties and dialects: Arabic as a case study

Published online by Cambridge University Press:  05 May 2020

Taghreed Tarmom ,
William Teahan ,
Eric Atwell  and
Mohammad Ammar Alsalka
Taghreed Tarmom
Affiliation:
School of Computing, University of Leeds, Leeds, UK
William Teahan
Affiliation:
School of Computer Science and Electronic Engineering, Bangor University, Bangor, UK
Eric Atwell*
Affiliation:
School of Computing, University of Leeds, Leeds, UK
Mohammad Ammar Alsalka
Affiliation:
School of Computing, University of Leeds, Leeds, UK
*
*Corresponding author. E-mail: [email protected]
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Abstract

The occurrence of code-switching in online communication, when a writer switches among multiple languages, presents a challenge for natural language processing tools, since they are designed for texts written in a single language. To answer the challenge, this paper presents detailed research on ways to detect code-switching in Arabic text automatically. We compare the prediction by partial matching (PPM) compression-based classifier, implemented in Tawa, and a traditional machine learning classifier sequential minimal optimization (SMO), implemented in Waikato Environment for Knowledge Analysis, working specifically on Arabic text taken from Facebook. Three experiments were conducted in order to: (1) detect code-switching among the Egyptian dialect and English; (2) detect code-switching among the Egyptian dialect, the Saudi dialect, and English; and (3) detect code-switching among the Egyptian dialect, the Saudi dialect, Modern Standard Arabic (MSA), and English. Our experiments showed that PPM achieved a higher accuracy rate than SMO with 99.8% versus 97.5% in the first experiment and 97.8% versus 80.7% in the second. In the third experiment, PPM achieved a lower accuracy rate than SMO with 53.2% versus 60.2%. Code-switching between Egyptian Arabic and English text is easiest to detect because Arabic and English are generally written in different character sets. It is more difficult to distinguish between Arabic dialects and MSA as these use the same character set, and most users of Arabic, especially Saudis and Egyptians, frequently mix MSA with their dialects. We also note that the MSA corpus used for training the MSA model may not represent MSA Facebook text well, being built from news websites. This paper also describes in detail the new Arabic corpora created for this research and our experiments.

Keywords

ArabicCorpus linguisticsLanguage resourcesMachine learningSublanguages and controlled languagesText segmentation
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 6: Natural Language Processing for Similar Languages, Varieties, and Dialects , November 2020 , pp. 663 - 676
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Copyright
© Cambridge University Press 2020

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