mohammad bahrani

Automated author identification is one of the important fields in forensic linguistics. In this study, the effectiveness of systemic functional grammar (Halliday and Matthiessen, 2014) features in Persian authorship attribution was compared with that of function words. First, a corpus composed of documents written by seven contemporary Iranian authors was collected. Second, a list of function words was extracted from the corpus. Moreover, conjunction, modality and comment adjunct system networks were applied to form a lexicon using linguistics resources. Then, the relative frequency of function words in addition to systemic functional features were calculated in each document. Multilayer perceptron classifier, a type of neural network, was used for learning phase which resulted in a desirable accuracy in evaluation phase. The results of the study showed that using function words method is superior to systemic functional approach alone in Persian author identification, however, simultaneous use of the two methods increases the effectiveness in comparison to each alone.

Nowadays, corpora are widely used in authorship attribution. In this research, a corpus of persian contemporary texts was applied to identify the authorship of texts and the effectiveness of function and content words in this task was compared. In order to reach this goal, seven contemporary writers named Hoshang Golshiri, Bozor Alavi, Ahmad Mahmoud, Mahmoud Dolatabadi, Nader Ebrahimi, Jalal Al Ahmad and Gholamhossein Saedi were selected and their books were collected. Then by using this corpus and deep learning algorithms like multilayer perceptron and Long Short Term Memory, effectiveness of function and content words was evaluated. The results of the research indicated that function words based method was superior to content words one in authorship attribution. In addition, pronouns, especially demonstrative and personal pronouns, showed the highest efficiency among the types of function words to determine the author of a text. Moreover, features based on conjunctions and auxiliary verbs were valuable to recognize persian writers.

Fake news detection using content features have attracted many researchers in the last few years. These approaches rely mainly on news datasets and analyzing their style and content. Although there are some fake news datasets in English, fake news detection in the Persian language suffers from the lack of suitable datasets. This article introduces a manually labeled Persian fake news dataset, containing about 5000 posts related to COVID-19 and extracted from Telegram messenger. The process of building the dataset is done in two stages: 1) data collection and pre-processing; and 2) labeling manually using a settled rule set and an established framework. In the labeling stage, seven tasks have been used for labeling, including: 1) Factual; 2) Hate, blame, and negative speech; 3) Rising moral, encouragement, and advise; 4) Political news; 5) Death statistics; 6) Cure, medicine, and health care; and 7) Worth to be considered for fact checking. For each labeling task, 3 labels including “Yes”, “No”, and “Can’t decide” are used. The main labeling task, i.e. “Factual” task is assigned to two annotators and in case of disagreement between annotators, the label assigned by third annotator is accepted. The kappa measure for inter-annotators agreement obtained equal to 0.706 that is in substantial range. This dataset is about 10 times larger in comparison to similar Persian datasets and can be used for not only fake news studies but also some other Persian Natural Language Processing (NLP) studies.

In today's world, software tools play an important role in speeding up software development, reducing development costs and human efforts, as well as increasing reliability. In software development by tools, choosing a suitable tool will be a difficult task because many of them are available with different capabilities. On the other hand, little research has focused on the classification of these tools and their comparison. This paper aims to perform a literature review of software development tools and to propose architectures for the requirement of the Organization of Small Industries and Industrial Towns of Iran (OSIITI), in Iran. We did a survey over more than 50 software development and programming tools. The results of this survey identified ten classes, namely (a) Database Tools; (b) Integrated Development Environment; (c) Software Development Frameworks; (d) Data Science Tools; (e) Source Control Tools; (f) DevOps Tools; (g) Unified modeling Language (UML) Tools; (h) Cloud Tools for Software Development; (h) Prototyping Tools; and (j) Notifications Programs. For each class, we collected the most software tools that are currently used with their major features. After that, two architectures, based on layered and service-oriented patterns are proposed for OSIITI. The ten specified classes, along with the tools in each class, are very useful for organizations like OSIITI who want to develop software, for both small and large projects.

With the advent of social media, social networks have also emerged as one of the newest management topics. Social networks play an essential role in promoting and circulating information in major sporting events. Concerning this, the purpose of this study is to investigate and compare how to promote the Rio 2016 and Tokyo 2020 Olympic Games on social media. The social network analysis approach used to examine the Games Promotion Network, for which all the tweets of the official page of the two Olympics extracted from Twitter. 2256 tweets obtained from the official page of the Rio Olympics and 3287 tweets from the official page of the Tokyo Olympics, which finally included 436 tweets from the Rio and 905 tweets from Tokyo. Findings showed that athletes were more numerous in both Olympics than other stakeholders regarding the number of actors in the network. At the same time, the communication weight of international sports organizations has been greater than that of other stakeholders. The present study showed that it is impossible to promote a sporting event on social media without employing multiple stakeholders. However, depending on the purpose of the sport and organizations, their pattern for brand or event promotion can be unique.

The popularity of the Olympic Games is so great that Games hosting tries to use a wide range of tools to protect the Olympic brand, achieve hosting goals, and connect with audiences. As one of the tools, social media has become an important part of media policies of sport events. The aim of the present study is to investigate the use of Twitter, as a social media in the 2020 Tokyo Olympic Games. In this study, the official twitter account of the 2020 Olympic Games Organizing Committee was reviewed from 2014 to the end of 2019. Finally, with the text-mining of 2,364 tweets published online at the timeline of the Tokyo 2020 account hashtags, tags, number of likes, retweets, and multimedia including photos, videos and gifs used in tweets were extracted. What is generally evident in Twitter-related research that the most important operator on this platform is retweets. Despite the increase in the number of retweets, the main goals of an organization can be achieved on Twitter. In fact, hashtags, hyperlinks, tag, and the use of multimedia content are used to increase user engagement in terms of the number of retweets.

Social media function is constantly increasing and sports researchers have attended to this role and function more than ago. The purpose of this study was to review all articles published in sports magazines during the last 10 years with a systematic review approach. The initial article database consisted of 156 selected articles, which according to the title and keywords included one of the terms of Social Media, Twitter, Facebook, Instagram, YouTube, Pinterest or Snapshot. Then, according to the publication date (2010-2019) and studying abstracts, 137 articles were selected and downloaded. The findings showed that the number of published articles and theories used in them has increased from 2010 to 2019. The diversity of theories supporting the reviewed researches suggests that sport is still considered a multifaceted field, but sporting events such as the World Cup and the Olympics are areas that receive less attention in sports social media studies.

Ezafe construction is considered as one of the most important issues in various linguistic theories including phonetics, morphology and syntax and many Iranian linguists have analyzed this phenomenon from these different aspects. Ezafe marker is usually not written in Persian text. So, not only does it result in a high degree of ambiguity in reading, analyzing, and understanding Persian documents, but also it causes serious difficulties for a large number of natural language processing tasks (NLP) such as part-of-speech (POS) tagging, Named-Entity Recognition (NER), Co-reference Resolution, Converting Text to Speech, Machine Translation, syntactic parsing and so on. As a result, determining the positions of Ezafe in a given sentence is viewed as a controversial and challenging issue especially in these applications. Using a corpus-based analysis and dependency grammar, the current paper sets to study Ezafe positions. Due to the fact that dependency grammar applies a simple parsing, uses low memory and speeds up computer operations, this grammar is regarded as one of the important and practical grammars in the field of computational linguistics. Accordingly, this study will use a rule-based method within this framework to recognize Ezafe positions. For this purpose, all Ezafe constructions which are provided in Uppsala Persian Dependency Corpus (UPDC) are analyzed based on dependency relations. In the next step, only seven Ezafe rules are formulated consisting of such non-verbal phrases as noun phrases, adjective phrases, prepositional phrases, adverb phrases, phrases with more than one post-modifier, phrases with more than one post-modifier as a phrase and co-ordinations. The proposed rules can be used in Persian dependency corpora and a great number of language processing tasks which are based on dependency relations. In addition, in the present research, Ezafe positions which have not been mentioned in previous theoretical and computational studies will be elaborated.

In this paper, an automatic method in converting a dependency parse tree into its equivalent phrase structure one is introduced for the Persian language. In the first step, a rule-based algorithm is designed. Then, Persian specific dependency-to-phrase structure conversion rules merge to the algorithm. Subsequently, the Persian dependency treebank with about 30,000 sentences is used as an input for the algorithm and an equivalent phrase structure treebank is extracted. Finally, the statistical Stanford parser is trained using the resulting treebank. Experimental results show a F1 of 96.05% for the conversion algorithm and an F1 of 86.01% for Persian factored model parser.

communities, cyberspace is vulnerable to attacks, false information and deception. It becomes increasingly important to design an efficient method to trace identity in this community. In order to investigate the problem of gender identification, we propose 48 features, and design three machine learning algorithms. The results of study showed that AD tree classifier had accuracy up to 73.8%.

Phonetic reduction is consistently considered as a challenge for speech recognition systems. Among them is the reduction of Persian telephonic numerals /do-noh/ (2-9)، /haft-haʃt/ (7-8) and /se-sefr/ (3-0). The acoustic analysis of the telephonic signals /se-sefr/ shows that the deletion of /r/ diminishes the phonological distinctions between [se-sef] pairs، however the phonetic cues in particularly vowel /e/ including vowel duration، F2 and F3 locus equations can still transmit this phonological contrast. Word recognition systems can recover the reduced forms by implementing these acoustic cues.

Spoken language understanding is considered as a specific domain of natural language understanding in which the uttered sentences are not as well-formed as written sentences. In the present paper, a text-based system of spoken language understanding is introduced for ticket reservation domain. This system is developed according to the datadriven approach and its architecture includes two main parts: first, extracting parameters of the model and second, assigning the most likely semantic tags to the sequence of words. "Hidden Markov Model" and "Viterbi" algorithm are applied in order to train the parameters and to tag the sequence of words. For this purpose, a corpus of commonly-used sentences in ticket reservation domain is collected and a specific tag is assigned to each word or a combination of words. In the training step, by using the tagged corpus, a sequence of possible tags is learned for a sequence of various words and in the testing step the most likely tag is assigned to a word or a combination of words according to the probabilities calculated in the previous step. Evaluation of the accuracy of system in recognizing the three key tags of departure, arrival and date is 91%.

In this paper, a continuous speech recognition system for the Persian language is introduced and the roles of acoustic and language models are examined. Context-independent and context-dependent acoustic models are used in the system and the results of their employment are presented. Moreover, word-based, POS-based and class-based triphone language models are extracted using Persian text corpus and incorporated in the speech recognition system. In addition, a grammatical language model based on GPSG is implemented in the system and is used in combination with the statistical language model. Experimental results demonstrated hat as expected, context-dependent phonetic models show the best performances. Also, the word-based triphone language model showed superiority over other statistical language models. Moreover, the combination of grammatical language models with statistical ones proved to lead to better recognition results. The system introduced in this paper is the first Persian speech recognition system capable of practical usage and is based on numerous research works performed for its design and implementation.