Persian Language

In this paper we analyzed some of the phonological rules of Russian loanword adaptation in Persian, on the view of Optimal Theory (OT) (Prince and Smolensky, 1993/2003). It is the first study of phonological process on Russian loanwords adaptation in Persian. By gathering about 50 current Russian loanwords, we selected some of them to analyze. We found out that vowel insertion, vowel prothesis, no palatalized consonants are the phonological processes occur on Russian loanwords in Persian.

Russian language, with regard to its grammar, is a language with its own special complexities, hardly found in other languages. Although languages are different from each other, they have some similarities as well. The Iranian learner, while learning Russian, analyzes the sentences using patterns similar to his/ her native language patterns. Occasionally, such native patterns cause the learner to make glaring mistakes. Verb, in Russian language, is a word that grammatically conducts a noun and its modifiers (падеж). The difficulty, which the learners encounter is the lack of a complete agreement of verb conduction. That is to say, it is possible for a verb in Persian language to conduct its dependent word in a particular manner, which the very same verb, in Russian language, might conduct its defendant word in a different manner. It is even possible that the verb conduction by the same verb in the two languages might occur by means of two totally different prepositions. In other to avoid such errors, it seems necessary to make the learners aware of their occurrences at the very initial steps of language learning.

The concept of tense is an important issue in the morphological and syntactic analysis of words and sentences. The connection between this concept and other parts of the sentence, including the verb, and understanding its structural, semantic and conceptual differences has always been considered by Russian and Persian linguists. In this article, various types of direct and figurative applications of the Russian present tense are studied considering their equivalent expressions in the Persian language. While the present tense exists in both mentioned languages, there remain distinct conceptual, semantic and structural differences, of which lack of knowledge leads to difficulties and mistakes in learning, translation and research. Realizing the research deficiency on this particular subject and based on the issues of teaching Russian language to Persian-speaking students, interesting results are pointed out in the case of direct and figurative application of the Russian present tense and its comparison in the Persian language.

Persian language and literature have a presence of about one thousand years in the Indian subcontinent. The growth of this language is indebted to Ghourid rulers whose mother tongue was Persian. However, we should not be negligent of the significant role of great Iranian Sufis and mystics such as Ali Ibn Othman Hojviri, Khawaja Moinuddin Chishti and Sayyed Ali Hamadani. The necessity of a research about the continuation and growth of Persian language in Pakistan is due to the questions as when this language and literature found its way and what is their importance in this country, as well as to what extent they have influenced Urdu and eventually their effects on Iranian studies. The research method is based on the review of historical documents with emphasis on numerous manuscripts kept in national and private libraries in Pakistan. The outcome of this research shows that the strength and solidity of Urdu is in critical need of more manuscript sources and stone-print documents for better understanding of spiritual culture, historical and public identity of Pakistan and seriously demands preservation of the ancient heritage of this region and production of new sources for the modern history and culture of Pakistan. Persian language and its heritage no doubt have a great share in this regard.

This article explores the polysemy of four negative non-verbal prefixes in Persian language (zedd ‎‎'against, opposite of', bi 'without', nā 'not' and qejr 'not, non-') based on Principled Polysemy ‎framework (Tyler and Evans 2001, 2003). First, the primary sense of each prefix is determined ‎and then it is explained how non-primary senses are derived from the primary one, hence ‎demonstrating the semantic network of each prefix as a radial category. In this research, using ‎AntConc software (Anthony, 2014), first all the occurrences of the four prefixes were extracted ‎from the Hamshahri Corpus Version 2 (AleAhmad, Amiri, Darrudi, Rahgozar & Oroumchian, ‎‎2009) and then in order to analyze research data, some of them were randomly selected. The ‎findings of the study indicate that only in three of the four prefixes under study, polysemy is ‎observed, and that the frequency of use, ease of derivation and predominance in the semantic ‎network are the best criteria for determining the primary sense. The conceptual phenomenon ‎involved in the polysemy of these prefixes is metonymy. Data analysis shows that metonymical ‎shift occurs at two levels: at the level of morpheme/prefix sense and at the level of word-‎formation, and that the former leads to more straightforward relations within the semantic ‎network. This is due to the fact that the latter requires a more complex line of imagination which ‎automatically translates into a corresponding complexity of relations in the semantic network and ‎significant reduction in the type frequency of non-primary senses as a whole.‎

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a recently proposed learning to rank data, aims to deal with such issues by the classifier fusion idea. CF-Rank generates a few click-through features, which provide a compact representation of a given primitive dataset. By constructing the primitive classifiers on each category of click-through features and aggregating their decisions by the use of information fusion techniques, CF-Rank has become a successful ranking algorithm in English datasets. In this paper, CF-Rank is customized for the Persian Web content. Evaluation results of this algorithm on the dotIR dataset indicate that the customized CF-Rank outperforms baseline rankings. Especially, the improvement is more noticeable at the top of ranked lists, which are observed most of the time by the Web users. According to the NDCG@1 and MAP evaluation criteria, comparing the CF-Rank with the preeminent baseline algorithm on the dotIR dataset indicates an improvement of 30 percent and 16.5 percent, respectively.

The task of extracting semantic relations from raw data is called relation extraction. One of the most important fields in open information extraction is the automatically extraction of relations in any domain, especially in web mining. There are many works and approaches for relation extraction in English and other languages. Some of these approaches are based on parsing trees. Dependency parsing in the Persian language is difficult and time-consuming, since Persian is a low resource language and has also a dependency grammar and lexical structure, which affects also the speed of relations extraction in Persian. In this paper we will introduce a fast relation extraction method in Persian called RePersian. RePersian is dependent on part-of-speech (POS) tags of a sentence and special relation patterns, which are extracted by analyzing sentence structures in Persian. For finding relation patterns, RePersian searches through POS-tags that are given in regular expression forms. By matching the correct POS pattern to a relation pattern, RePersian extracts the semantic relations in a sentence. We appraise RePersian in two different scenarios on the Dadegan Persian dependency tree dataset. RePersian had on average the precisions 78.05%, 80.4% and 54.85% in finding the first argument on a relation, the second argument and the right relation between them.

With the fast growth of social media, they have become the most important platform for posting multimodal content generated by users. Much of the data on social networks such as Instagram and Telegram is multimodal data. With the aim of analyzing such multimodal data in social networks, multimodal sentiment analysis has become one of the most significant subjects for researchers in the field of emotion recognition and data mining. Although multimodal sentiment analysis of social media data for English language has been addressed in several researches recently, few studies addressed the problem for the Persian language which is the official language of more than 120 million of people around the word. In this study, a multimodal deep learning model is proposed to address this problem. The proposed method utilizes a bi-directional long short-term memory (bi-LSTM) for processing text posts and a VGG16 convolutional network for analyzing images. A new dataset of Instagram and Telegram posts, MPerSocial, containing 1000 pairs of images and Persian comments is introduced in the current study and used for evaluating the proposed method. The results of experiments show that using the fusion of textual and image modalities improves sentiment polarity detection accuracy by 20% and 8% compared with the scenario in which image and text modalities in isolation. Also, the performance of the proposed model is better than three similar deep and four traditional machine learning models. All codes and dataset used in the current study are publicly available at GitHub.  

Iconology is a type of scientific study in which the worldview of the creation era of visual works is determined. This type of cultural studies includes the description of the visual patterns of the work (pre-iconography), the introduction of the narrative or narratives that are observed in the work (iconography analysis) and finally the interpretation of iconology. The purpose of this study is to examine the written content engraved on Safavid coins using the iconographic analysis method. To fulfill this purpose, all the texts engraved on Safavid coins are described, and then, using the method of iconography or iconography analysis, the narratives about these texts are stated. Moreover, the iconographic analysis of the written content engraved on Safavid coins shows some of the political, cultural and religious developments of this era, such as the position of Hamza Mirza, the son of Shah Muhammad, in the developments of Safavid era, the religious approach of Ismail II, the continuity of Safavid position in Iran's internal developments after Afghan invasion, and the high position of Shiite religion and the Persian language during Safavid era.

Previous studies have demonstrated the efficacy of speech rhythm measures in speaker identification across various languages with different phonotactic structures. In Persian language, in particular, two categories of speech rhythm metrics were examined: duration-based and intensity-based metrics. Building upon these prior works, the current study delves deeper into the discrimination capabilities of the mentioned measurement types—duration-based versus intensity-based—in the context of Persian speakers. To achieve this, a multinomial logistic regression model was employed on a dataset comprising 20 male Persian speakers, each reciting 100 sentences at a normal speaking pace. Findings revealed that, when distinguishing between Persian speakers, duration-based measures outperform intensity-based ones, however, this excellence is very slight. This observation is significant, as it sheds light on the suitability of specific rhythm metrics for Persian speaker identification. I postulate that this discrepancy in performance may be attributed to the simple syllable structure of Persian and the lesser reliance on intensity as a primary indicator of lexical stress. This research contributes valuable insights into the choice of rhythm metrics for optimal Persian speaker identification and underscores the importance of considering linguistic features when developing speaker recognition systems.Top of Form

This article investigates the comitative constructions in Persian. These constructions consist of three members named accompanion, accompanee and accompanying setting. Previously, studies considered ‘bā’ (WITH) as a preposition in traditional grammar. All traditional grammarians introduced ‘bā’ as a preposition except those who pointed to ‘bā’ (WITH) in accompanying situations. One of the aims of the present research is to answer this question: Is ‘bā’(WITH)  a preposition in all sentences or  is it a conjunction or both? So, this study attempts to show its syntactic and functional features in Persian. Another goal is to compare coordinated and comitative constructions. The hypothesis is that ‘bā’(WITH) can be considered as a conjunctor and a preposition as well. Thus comitative constructions are divided into two groups: 1) symmetrical constructions in which [ with + DP2 ] is not an adjunct (an argument). The type of the verbs in this group are as follows: collectives, combining, relational predicates and similar comparison.  2) asymmetrical constructions in which [ with + DP2 ]  is an adjunct. The second type does not depend on the type of the verbs. It consists some members named non-collective verbs, instrumental, body organ, transportation. The instrumental type are of mediatory and facilitating. The findings of the present article can be used in the teaching of Persian grammar as well as syntactic analyses.