Abstract The myth is a story that is passed on to the generations generation after generation, ridiculed by literary writers in their literary work; to convey an important idea to the recipient, and in legend many things useful to the creator, especially the novelist, from that ritual and harnesses to be symbols that reach the recipient what the creator thinks; The Arabic novel, I chose Sahel al-Nahr's novel of Sudanese novelist Buthaina Khader Makki, a well-known author in her country for her advocacy of women and their rights. She used legend and mythic symbols in a deliberate manner to convince the recipient that what he read was a real thing worth standing at Understand E, has reached a number of results, including Buthaina Khader Makki has made reality a myth and legend. : The novelist Buthaina Khodr Mekki narrated her story on the myth that was equivalent to the idea of differences between women and men, no matter how much she was a follower. The novelist resorted to the myth to try to give a convincing explanation for the strangeness of the reality presented in her life affected by it, and affected it; to have an eye different from the eye of others. The novel identified aspects of the use of myth in the novel are: Escape from the strength and pressure of current problems. And the explosion of literary connotations and transmitted to the mind of the recipient, to match the culture of the recipient and his knowledge of things, myth is a method of thrill. The myth was the idea of the novel and its characters; it was a collection of symbols and rituals that illustrated the message that the novelist wanted to convey. Finally, I hope that I have succeeded in trying to deliver an idea of Sudanese literature rich in creativity, thought and serious work to the recipients, perhaps to draw the attention of researchers to study more and more to explore the treasures.
This paper is focusing on reducing the time for text processing operations by taking the advantage of enumerating each string using the multi hashing methodology. Text analysis is an important subject for any system that deals with strings (sequences of characters from an alphabet) and text processing (e.g., word-processor, text editor and other text manipulation systems). Many problems have been arisen when dealing with string operations which consist of an unfixed number of characters (e.g., the execution time); this due to the overhead embedded-operations (like, symbols matching and conversion operations). The execution time largely depends on the string characteristics; especially its length (i.e., the number of characters consisting
... Show MoreCurrently, the prominence of automatic multi document summarization task belongs to the information rapid increasing on the Internet. Automatic document summarization technology is progressing and may offer a solution to the problem of information overload.
Automatic text summarization system has the challenge of producing a high quality summary. In this study, the design of generic text summarization model based on sentence extraction has been redirected into a more semantic measure reflecting individually the two significant objectives: content coverage and diversity when generating summaries from multiple documents as an explicit optimization model. The proposed two models have been then coupled and def
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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural
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Rumors are typically described as remarks whose true value is unknown. A rumor on social media has the potential to spread erroneous information to a large group of individuals. Those false facts will influence decision-making in a variety of societies. In online social media, where enormous amounts of information are simply distributed over a large network of sources with unverified authority, detecting rumors is critical. This research proposes that rumor detection be done using Natural Language Processing (NLP) tools as well as six distinct Machine Learning (ML) methods (Nave Bayes (NB), random forest (RF), K-nearest neighbor (KNN), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and Decision Tree (
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Idiomatic expressions in Russian journalism make one important borrowed means for making a dialogue with the receiver's intellect in so far as it has the distinct feature of having clarity and exactness of meaning. The meaning is seen as a shortcut for covering a series of concepts and details so as to arrive at the intended meaning. This is done by stimulating the reader by the use of certain clear idioms. The use of such idioms in a journalistic text is not for a linguistic purpose only, but it is a cultural and social phenomenon reflecting the type of current changes in the society and it aims at discoursing with the reader's mind. This paper is a practi
... Show MoreIn recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor
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Translation is both a social and cultural phenomenon, it can neither exist outside a social community and it is within society, nor it can be viewed as a medium of cross-cultural fertilization. This paper aims to investigate the difficulties that a translator may face when dealing with legal texts such as marriage and divorce contracts. These difficulties can be classified according to the present paper into syntactic, semantic, and cultural. The syntactic difficulties include word order, syntactic arrangement, unusual sentence structure, the use of model verbs in English, and difference in legal system. As to the semantic difficulties, they involve lack of established terminology, finding functional and lexical equivalence, word for word t
... Show MoreField of translation is sampled with many types of translation, such as the literary, scientific, medical, etc. The translation of grammatical aspects has always been with difficulties.
Political translation is the focus here. There are many general problems faced by translators when translating political texts from Arabic into Spanish. The aim here is to clarify the definition of functions or terms within the text, and to arrive at the correct from of translation of such texts from Spanish into Arabic. It is worth mentioning that the paper is of two parts: the first exemplifies what is meant by translation, the prerequisites of a translator, along with mentioning the methods followed&nbs
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Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as t
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