Every single word and sentence of eloquent words have been selected and arranged in a purposeful and logical way. The Quranic researchers have sought to discover the way of relation and the philosophy of the arrangement (order and formation) of the Qur'anic verses since the Quran is the most eloquent word. The process (Semantic relations) is also defined in this way in order to provide a simple understanding of how the verses of the Qur'anic chapters are related. The content of each sura is divided into several Semantic domains and the relationship between these relations is examined based on this new process. In this paper, the process (Semantic relations) is adapted to the two suras (Arabic: سورة, chapters) of Ash-Shams (Arabic: الشمس) and al-Layl (Arabic: اللیل) in a descriptive analytical method to find an answer to the question of how the application of the process of Semantic relations leads to the discovery of the textual coherence of Suras Ash-Shams and al-Layl. Finally, it is concluded that there is a central Semantic relation in each of these two surahs that the rest of the relations are arranged in different methods of expressing in order to prove or believability and better understanding of that central meaning.

This article is an attempt to give pragmatic criteria for the recognition of the polysemy and homonymy. The difference between polysemy words and the homonymy ones, is one of the common issues in books and some articles of Semantics, but the criteria that linguists have put forward in this regard is not unpopular when tested. The manner in which this paper proposes to re-examine the causes of polysemy in words. By providing examples of poetry Dehkhoda’ s dictionary, we emphasized the conjugal relations of words and tissues of the language, in which the term is contained. therefore, it is possible to use the same criteria in the separation of the polysemy and homonymy. However, some of the meanings that the authors of the Dehkhoda’ s dictionary have brought can be a source of doubt, but a lot of evidence is useful. We did not refer to the texts because of the choice of the dictionary.

Exploiting Semantic content of texts due to its wide range of applications such as finding related documents to a query, document classification and computing Semantic similarity of documents has always been an important and challenging issue in Natural Language Processing. In this paper, using Wikipedia corpus and organizing it by three-dimensional tensor structure, a novel corpus-based approach for computing Semantic similarity of texts is proposed. For this purpose, first the Semantic vector of available words in documents are obtained from the vector space derived from available words in Wikipedia articles, then the Semantic vector of documents is formed according to their words vector. Consequently, Semantic similarity of a pair of documents is computed by comparing their corresponding Semantic vectors. Moreover, due to existence of high dimensional vectors, the vector space of Wikipedia corpus will cause curse of dimensionality. On the other hand, vectors in high-dimension space are Usually very similar to each other. In this way, it would be meaningless and vain to identify the most appropriate Semantic vector for the words. Therefore, the proposed approach tries to improve the effect of the curse of dimensionality by reducing the vector space dimensions through random indexing. Moreover, the random indexing makes significant improvement in memory consumption of the proposed approach by reducing the vector space dimensions. Additionally, the capability of addressing synonymous and polysemous words will be feasible in the proposed approach by means of the structured co-occurrence through random indexing.

In this article, a new approach to the theory of Semantic Networks has been presented, that is a new multidimensional model called Semantic Sphere.Semantic Sphere is a formal structure to represent how concepts are stored in human’s mind. In this model, like Semantic Networks, nodes and lines are to show the concepts and their relations in the mental Semantic structure, but the nature and the structure of these nodes and lines are different from that of Semantic Networks.The most important properties of Semantic Sphere are: firstly, hierarchical relations have secondary functions; therefore, there is no necessity to keep hierarchical organization. Secondly, the relations are not only unilateral, but they may be bilateral or even without direction just to represent simple relations between two concepts. Thirdly, concepts are differentiated from words and proposition. Having these properties of Semantic Sphere in mind, some of the main problems of Semantic Networks, namely, inability to show the flexibility of changing concepts, not being consistent with the basic hypotheses of Semantic networks as one word - one position, inadequacy of distinguishing between words and concepts, etc. are supposed to be satisfied.Though this new model is in its infancy, it seems that it has the potentiality to be a substituted model for known Semantic Networks, since its basic hypotheses are based not only on abstract facts, but on some of the concrete facts of conceptual properties and also on some of the physical properties of the human brain.

Objectives: The present study was designed to investigate the automatic activation of Semantic priming in schizophrenic patients. Method: 36 schizophrenic patients and 36 normal subjects participated in two experiments. In experiment one, the effect of Semantic relation on identification of degraded targets was examined between a series of single prime words and single target words presented in a typical Semantic priming paradigm. To restrict the priming to automatic processes, in experiment two, series of two primes were presented simultaneously instead of one. Both primes were related to the target, and the effect of Semantic relation between two primes on identification of degraded targets was examined. Finding: In experiment one, both groups demonstrated Semantic priming effect for related words; there was no significant difference between the two groups. In experiment two, Semantic relation between two primes resulted in a significant priming effect in normal subjects, but not in schizophrenic patients. Results: This study showed that schizophrenic subjects have difficulties in automatically activating related words in their Semantic networks. Restricting Semantic priming to automatic processes can suggest a way to resolve the inconsistencies in studies with schizophrenic subjects.