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Embedded English verbs in Arabic-English code-switching in Egypt

Domaine:

natural language processing

Type de record:

paper
Créateur:
MałMag
Éditeur:
SAG
Hôte:
Aims: This study provides new insights into Arabic-English code-switching with particular reference to verb insertion. It aims to identify (1) patterns of English verb insertion into Arabic; (2) factors affecting them. We offer an alternative to previous studies’ conclusions regarding a supposed lack of English verbs integrated morphologically into Arabic, which is claimed to result from incongruence between Arabic and English verb systems. Methodology: We employ the Matrix Language Frame (MLF) model and the 4-M model. Data and analysis: The data comprise 14,414 clauses obtained from interviews with students at the American University in Cairo. Data were analyzed quantitatively. Findings: Most (80.17%) of inserted verbs were inflected with Arabic tense, gender, and number prefixes showing morphological integration into Arabic. We distinguished four recurrent patterns in verb insertion: (1) complete morphological integration in the present tense; (2) incomplete assimilation of forms requiring the use of the plural suffix -u; (3) lack of morphological integration in the past tense; and (4) lack of suffixation of Arabic clitics to English verbs. Originality: This is the first study focusing on verb insertion in Arabic-English code-switching based on empirical data collected in Egypt. It offers different findings on verb patterns and their explanation compared with other quantitative studies based on the MLF model. We propose to look beyond incongruence between Arabic and English as a factor determining verb patterns to include linguistic convention. Thus, we hypothesize that verb insertion might be controlled by linguistic norms accepted and perpetuated in a given speech community. Significance: Contrary to previous claims, our results show that patterns of verb insertion in Arabic-English code-switching are consistent with the MLF model. Hence, the study contributes evidence for the MLF model and its explanatory value.

Visit

doi.org

Tasks

code switching

Licenses

https://creativecommons.org/licenses/by-nc/4.0/

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