In this article, the writers make a distinction between two types of Text, open Text and closed Text. While the latter has a fixed and limited meaning, fixed signifiers and signifieds, with the signs always referring the reader to a fixed world, the former has the potential to be loaded with various meanings. To explicate the two types of Text and the factors that make a Text open, the writers draw on the views of great thinkers such as Rolan Barthes, Wolfgang Izer, Jacques Derrida and Carl Gustav Yung. The purpose of the article is to show how a familiarity with the western interpretations of Hermeneutics can affect our appreciation of Persian literature.

In this paper one of the main forms of Textualization will be represented; little known, in our opinion, first by foreign language specialists (linguists or literary) in Iran and marginalized, even absent, from the educational applications in language and literature courses in the Iranian universities.After an overview of the types of Texts, we will try to develop the idea that concerns us here, namely the study of argumentative type in two ways: firstly the epistemological concept of argumentation and then the organization of the argumentative Text. French: Le Texte argumentatif et la typologie de TextesResume Cet article aura comme but de representer l’une des formes principales de Textualisation, peu connue, a notre avis, d’abord par les specialistes des langues etrangeres (linguiste ou litteraire) en Iran et marginalisee, et meme absente, par consequent, des applications pedagogiques dans les cours de langue et litterature au milieu universitaire iranien.Apres un apercu general de la typologie des Textes, nous avons essaye de mettre au point l’idee qui nous preoccupe ici, a savoir l’etude du type argumentatif sur deux plans: d’abord plan epistemologique de la notion d’argumentation et ensuite plan organisationnel du Texte argumentatif.keywords: Typologie de Textes, Argumentation, Discours Argumentatif, Organisation de Texte, Categories de la Langue

One of the most important information security techniques is the hiding of information. Steganography is the art and science of hiding information in the cover of data (in the form of Text, image, video, or audio) such that it does not arise any suspicions, and is difficult or even impossible to discover. This paper presents a method for steganography in the form of Text which uses the methods of Text justifying in typing editors. The method presented in this paper is able to hide information better than some of previous algorithms in this field. This algorithm is resistant to various forms of attack such as visual, structural and statistical attacks. Another important capability of this method is that it can be used to send printed information.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of Textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is Text summarization. Summarizing Textual information can extract the main content of Text within a short time. In this paper, we propose an approach for extractive summarization on Persian Texts by using sentences embedding and a sparse coding framework. Most previous works focuses on Text’s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the Text’s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole Text with least reconstruction error. The proposed approach is evaluated on Persian dataset Pasokh and achieved 10. 02% and 8. 65% improvement compared to the state-of-the-art methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the Text’s sentences can lead us to better sentence summarization.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of Textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is Text summarization. Summarizing Textual information can extract the main content of Text within a short time. In this paper, we propose an approach for extractive summarization on Persian Texts by using sentences embedding and a sparse coding framework. Most previous works focuses on Text’ s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the Text’ s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole Text with least reconstruction error. The proposed approach is evaluated on Persian dataset Pasokh and achieved 10. 02% and 8. 65% improvement compared to the state-of-theart methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the Text’ s sentences can lead us to better sentence summarization.

This paper introduces readability or Text difficulty level and the factors contributing thereto from educational perspective in general and for translator training purposes in particular. Two most-frequently applied readability formulas, Flesch and Fog, are presented including the criticisms leveled by translation researchers and linguists against such quantifications. Subsequently, the question of Text difficulty is discussed from translation studies perspective following which Textuality standards, Text type and Text analysis for translator training purposes is put forth. The paper concludes with the introduction of five translation difficulties and problem areas as features presumably shared universally by all languages including Persian. Presented in the final section of this paper is certain guidelines for Text selection and classification in educational settings and for pedagogical purposes; Texts which are compatible with translational competence level of the translation trainees.

The aim of this article is to make a picture of the general cultural-intellectual circumstances of the time in terms of the relation contemporary man finds between himself and the ‘truth’. One way to grasp this relation is obviously by putting the subject into its historical perspective. We begin the inquiry with the genealogy of the tradition of scribe and producing the Text-as its achievement-in archaic and modern cultures. ‘Book’ has been taken to be the symbol of the source of the truth, and in movies, fairy tales, paintings, poems and literature, has always -been the sign of knowledge or truth itself. That is why the Text is an appropriate departure point. We take it for granted the relationship man finds between Text and the truth is subject to the very relation he finds between himself and the truth. Thus, studying the contemporary human's concept of the place of himself before the truth, we have no option but to deal with hermeneutics. Since this inquiry is not in line with only our linguistic interests, but with our philosophical question (vicinity or distance man feels between himself and the truth), we choose by nature philosophical hermeneutics among several hermeneutical approaches; in Heidegger's philosophical hermeneutics all consequences find ontological implications.The ultimate answer turns out to be that contemporary man considers he has have a very good chance of finding the truth directly and meeting it face to face immediately. It is important to note that this article attempts describe cultural conditions disinterestedly and make no issue of Heidegger's philosophy at all.

Text Classification is an important research field in information retrieval and Text mining. The main task in Text classification is to assign Text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian Text classifier is an appropriate approach to deal with different word formats and new words.Also, fuzzy theory may be used to manage uncertainty in imprecise Persian sentences.In this paper, we utilize L-R type fuzzy numbers in Bayesian Text classifier to classify Textual Persian documents (Fuzzy Bayesian Text classifier). The obtained results on simulated imprecise Textual Persian documents show improvements in both recall and precision parameters by using Fuzzy Bayesian Text classification approach over Naïve Bayesian Text classifier.