SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
Browse
2 results
Search Results
Item Open Access Saudi Bilinguals’ Language Preferences for Emotional Expression: Exploring Their Language Emotional Resonance and Codeswitching Habits(Saudi Digital Library, 2023-12-01) Alghamdi, Shahad Abdulaziz Hassan; Dewaele, Jean-MarcThe present study follows the wave of research regarding language and emotions (Harris et al., 2003; Dewaele, 2004b, 2006, 2010; Panicacci & Dewaele, 2018; Dewaele et al., 2023) by investigating the relationship between Saudi bilinguals’ language emotional resonance (LER) and codeswitching (CS) habits on their language preferences for emotional expression. It examines the effects of sociobiographical factors (gender and education level), linguistic factors (frequency of use, proficiency levels, and language dominance) of their Arabic L1 and English L2, and topic (personal/emotional, taboo/swearwords, religious, and hobbies and interests) and interlocutor (family, friends, colleagues, and strangers) types on the mentioned dependent variables. 172 Saudi participants filled out an online questionnaire adapted from the BEQ (Dewaele & Pavlenko, 2001-2003) and the RER-LX scale (Toivo et al., 2022). The collected data went under quantitative descriptive analyses. The findings elucidated that females had higher LER for the second language (L2) and CS more frequently, and participants with lower degrees experienced more CS. Moreover, participants who were more proficient in the L2 and used it often still preferred the first language (L1) for emotional expression. Furthermore, topic and interlocutor types majorly affected CS frequency, and L1 had higher LER and is most participants' preferred language for emotional expression. Finally, the participants were shown to have reduced emotional resonance (RER) for the L2 and used it as a distancing mechanism. This study represents the dynamic nature between LER and CS for bilingual speakers. The implications of this dissertation suggest increasing the sample size, including age of acquisition (AoA) and context of acquisition (CoA) as preliminary variables, and employing proficiency assessments for better accuracy of results in future research.78 0Item Restricted English-Arabic Cross-Language Plagiarism Detection(2022) Alotaibi, Naif; Joy, MikeThe advancement of the information era and technology has contributed to the rapid growth of digital text libraries and automatic machine translation systems. The machine translation tools facilitate translating texts from one language into another. Those have resulted in increasing the content accessible in different languages, which makes it easy to perform translated plagiarism, which is referred to as “cross-language plagiarism”. Identification of plagiarism amongst texts in different languages is more challenging than recognizing plagiarism within a corpus written in the same language. This research proposes a new framework for enhancing English-Arabic cross-language plagiarism detection at the sentence level. The framework comprises of two phases: the first phase is feature extraction, while the second is plagiarism detection based on a supervised machine learning classification model. Phase one is concerned with extracted features among English-Arabic cross-language sentences, where we propose approaches to extracting sets of features at lexical, semantic and syntactic levels. This phase involves two components. The first relies on translation plus a monolingual, pretrained word embedding model, integrated with term frequency inverse document frequency (TFIDF), and part of speech (POS) scheme methods, as well as word order information. The second component employs a pre-trained multilingual model for determining semantic relatedness between cross-language sentence pairs. In terms of the second phase, we propose to apply and examine using various supervised machine learning classifier methods, along with the extracted features and with combinations of those features to assist in the task of classifying sentences as either plagiarized or non-plagiarized. Each phase was assessed using different datasets. The experimental results for phase one on different benchmark datasets, such as SemEval-2017, show the proposed methods for extracted features achieved improvement when compared against the baselines and other methods. Analysis of experimental data for phase two demonstrates that using extracted features and their combinations with various supervised machine learning classification methods achieves promising results. Ultimately, using the combination of extracted features along with a supervised ensemble machine learning classifier achieves the best classification results.47 0