<p class="1"><span lang="X-NONE">This article is concerned with exploring conflicting media positions as reflected in the discursive patterns of news headlines and leads. Using Halliday’s transitivity analysis, this study examines how the Russian Military Intervention in the Syrian Civil War was socially, discursively and linguistically represented in the CNN and RT coverage of the event. The analysis examines the process of news making, role of ideology, and types of relationships between the news agencies and the political institutions in the United States and Russia. The aim is to show the discursive power of news agencies in creating different realities of the same event through language use. Results indicate that media are a political actor in the dissemination of both Russian and American views on the Syrian conflict. Although RT and CNN write about the same issue, the language choices made and underlying ideologies are different. The conflicting ideologies of both CNN and RT were highlighted by the use of positive self-presentation and negative other-presentation in order to support self’s ideological positions and distort other’s political stances.</span></p>

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In this study, the researcher has advocated the importance of human intelligence in language learning since software or any Learning Management System (LMS) cannot be programmed to understand the human context as well as all the linguistic structures contextually. This study examined the extent to which language learning is perilous to machine learning and its programs such as Artificial Intelligence (AI), Pattern Recognition, and Image Analysis used in much assistive learning techniques such as voice detection, face detection and recognition, personalized assistants, besides language learning programs. The researchers argue that language learning is closely associated with human intelligence, human neural networks and no computers or software can claim to replace or replicate those functions of human brain. This study thus posed a challenge to natural language processing (NLP) techniques that claimed having taught a computer how to understand the way humans learn, to understand text without any clue or calculation, to realize the ambiguity in human languages in terms of the juxtaposition between the context and the meaning, and also to automate the language learning process between computers and humans. The study cites evidence of deficiencies in such machine learning software and gadgets to prove that in spite of all technological advancements there remain areas of human brain and human intelligence where a computer or its software cannot enter. These deficiencies highlight the limitations of AI and super intelligence systems of machines to prove that human intelligence would always remain superior.

This study compares the translation outputs of an English into Arabic text using the three machine translators of Google Translate, Microsoft Bing, and Ginger. To carry this evaluation of the machine translation (MT) outputs, an English text and its Arabic counterpart were selected from the UN records. The English source text was segmented into 84 semantic chunks. Depending on the Arabic counterpart model text, each chunk was rated as "correct or incorrect" at the two levels of the translation attributes: fidelity and intelligibility. To perform the quantitative description of the evaluation process, the numbers of fidelity and intelligibility errors and their percentages were calculated. Results of this evaluation process revealed that none of the three translated versions of the source text was perfectly translated. Although the translation of Microsoft Bing was rated the best, Google's translation was found the least accurate due to the high percentage of fidelity and intelligibility errors detected in its translation output. However, the quality of Ginger's translation was found slightly less accurate than that of Microsoft Bing, but remarkably better than Google's translation. The findings of this study imply that these MT applications can be implemented to perform English into Arabic translation to get the broad gist of a source text, but a deep and thorough post-editing process looks essential for a full and accurate understanding of an English into Arabic MT output. The study recommends that more studies are encouraged to continue to assess the quality of MT that will further highlight its weaknesses and the strategies that should be adopted to overcome them.

This study attempted to answer the following research questions related to the various vocabulary discovery strategies which are used by Saudi undergraduate learners to find unknown word meanings, the most and the least vocabulary discovery strategies the learners used to discover unknown word meanings, the relationship between the type of Vocabulary Learning Strategies used and the scores the learners accomplished on the vocabulary test, and effectiveness of the learner control and the teacher control strategy in enhancing learners' ability to discover meanings of unknown words. Answering these questions of the study are expected to help language instructors determine the most feasible vocabulary learning strategies to help their students improve their vocabulary and so their language competences. Through purposive sampling, a group of 50 male students participated in this descriptive and analytic type of study. A questionnaire and a vocabulary test were used to collect data. The findings of the study revealed that in understanding a reading text, EFL Saudi students tend to figure out the meanings of unknown words, mainly by guessing word-meanings through different sub-strategies. The least used was the social interaction strategy. It was also found that students' scores on the vocabulary test significantly correlated (positively and negatively) with the type of strategy they used. This study concluded that it is vital for teachers and textbook writers to design more activities to train students on the use of effective vocabulary learning strategies, mainly guessing through socially linked contextual clues.