List of Articles Persian

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  1 - Blog feed search in Persian Blogosphere
  Mohammad Sadegh Zahedi Abolfazl Aleahmad rahgozar rahgozar Farhad Oroumchian
  10.7508/jist.2014.04.004
  Blogs are one of the main user generated content on the web. So, it is necessary to present retrieval algorithms to the meet information need of weblog users. The goal of blog feed search is to rank blogs regarding their recurrent relevance to the topic of the query. I More

  Blogs are one of the main user generated content on the web. So, it is necessary to present retrieval algorithms to the meet information need of weblog users. The goal of blog feed search is to rank blogs regarding their recurrent relevance to the topic of the query. In this paper, the state-of-the-art blog retrieval methods are surveyed and then they are evaluated and compared in Persian blogosphere. Also, one of the best retrieval models is optimized by using data fusion methods. Evaluation of the proposed algorithm is carried out based on a standard Persian weblogs dataset with 45 diverse queries. Our comparisons show considerable improvement over existing blog retrieval algorithms. Manuscript profile
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  2 - Opinion Mining in Persian Language Using Supervised Algorithms
  Saeedeh Alimardani abdollah aghaei
  10.7508/jist.2015.03.001
  Rapid growth of Internet results in large amount of user-generated contents in social media, forums, blogs, and etc. Automatic analysis of this content is needed to extract valuable information from these contents. Opinion mining is a process of analyzing opinions, sent More

  Rapid growth of Internet results in large amount of user-generated contents in social media, forums, blogs, and etc. Automatic analysis of this content is needed to extract valuable information from these contents. Opinion mining is a process of analyzing opinions, sentiments and emotions to recognize people’s preferences about different subjects. One of the main tasks of opinion mining is classifying a text document into positive or negative classes. Most of the researches in this field applied opinion mining for English language. Although Persian language is spoken in different countries, but there are few studies for opinion mining in Persian language. In this article, a comprehensive study of opinion mining for Persian language is conducted to examine performance of opinion mining in different conditions. First we create a Persian SentiWordNet using Persian WordNet. Then this lexicon is used to weight features. Results of applying three machine learning algorithms Support vector machine (SVM), naive Bayes (NB) and logistic regression are compared before and after weighting by lexicon. Experiments show support vector machine and logistic regression achieve better results in most cases and applying SO (semantic orientation) improves the accuracy of logistic regression. Increasing number of instances and using unbalanced dataset has a positive effect on the performance of opinion mining. Generally this research provides better results comparing to other researches in opinion mining of Persian language. Manuscript profile
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  3 - A fuzzy approach for ambiguity reducing in text similarity estimation (case study: Persian web contents)
  Hamid Ahangarbahan gholamali montazer
  10.7508/jist.2015.04.002
  Finding similar web contents have great efficiency in academic community and software systems. There are many methods and metrics in literature to measure the extent of text similarity among various documents and some its application especially in plagiarism detection s More

  Finding similar web contents have great efficiency in academic community and software systems. There are many methods and metrics in literature to measure the extent of text similarity among various documents and some its application especially in plagiarism detection systems. However, most of them do not take ambiguity inherent in word or text pair’s comparison as well as structural features into account. As a result, pervious methods did not have enough accuracy to deal vague information. So using structural features and considering ambiguity inherent word improve the identification of similar contents. In this paper, a new method has been proposed that taking lexical and structural features in text similarity measures into consideration. After preprocessing and removing stopwords, each text was divided into general words and domain-specific knowledge words. Then, the two lexical and structural fuzzy inference systems were designed to assess lexical and structural text similarity. The proposed method has been evaluated on Persian paper abstracts of International Conference on e-Learning and e-Teaching (ICELET) Corpus. The results shows that the proposed method can achieve a rate of 75% in terms of precision and can detect 81% of the similar cases. Manuscript profile
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  4 - Rough Sets Theory with Deep Learning for Tracking in Natural Interaction with Deaf
  Mohammad Ebrahimi Hossein Ebrahimpour-Komeleh
  10.52547/jist.16060.10.39.211
  20.1001.1.23221437.2022.10.39.7.1
  Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one of the most fundamental problems in computer vision, and use in a long list of applications such as sign languages recognition. Despite great advances in recent More

  Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one of the most fundamental problems in computer vision, and use in a long list of applications such as sign languages recognition. Despite great advances in recent years, tracking remains challenging due to many factors including occlusion, scale variation, etc. The mistake detecting of head or left hand instead of right hand in overlapping are, modes like this, and due to the uncertainty of the hand area over the deaf news video frames; we proposed two methods: first, tracking using particle filter and second tracking using the idea of the rough set theory in granular information with deep neural network. We proposed the method for Combination the Rough Set with Deep Neural Network and used for in Hand/Head Tracking in Video Signal DeafNews. We develop a tracking system for Deaf News. We used rough set theory to increase the accuracy of skin segmentation in video signal. Using deep neural network, we extracted inherent relationships available in the frame pixels and generalized the achieved features to tracking. The system proposed is tested on the 33 of Deaf News with 100 different words and 1927 video files for words then recall, MOTA and MOTP values are obtained. Manuscript profile
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  5 - Persian Ezafe Recognition Using Neural Approaches
  Habibollah Asghari Heshaam Faili
  10.61186/jist.44445.12.45.72
  Persian Ezafe Recognition aims to automatically identify the occurrences of Ezafe (short vowel /e/) which should be pronounced but usually is not orthographically represented. This task is similar to the task of diacritization and vowel restoration in Arabic. Ezafe reco More

  Persian Ezafe Recognition aims to automatically identify the occurrences of Ezafe (short vowel /e/) which should be pronounced but usually is not orthographically represented. This task is similar to the task of diacritization and vowel restoration in Arabic. Ezafe recognition can be used in spelling disambiguation in Text to Speech Systems (TTS) and various other language processing tasks such as syntactic parsing and semantic role labeling. In this paper, we propose two neural approaches for the automatic recognition of Ezafe markers in Persian texts. We have tackled the Ezafe recognition task by using a Neural Sequence Labeling method and a Neural Machine Translation (NMT) approach as well. Some syntactic features are proposed to be exploited in the neural models. We have used various combinations of lexical features such as word forms, Part of Speech Tags, and ending letter of the words to be applied to the models. These features were statistically derived using a large annotated Persian text corpus and were optimized by a forward selection method. In order to evaluate the performance of our approaches, we examined nine baseline models including state-of-the-art approaches for recognition of Ezafe markers in Persian text. Our experiments on Persian Ezafe recognition based on neural approaches employing some optimized features into the models show that they can drastically improve the results of the baselines. They can also achieve better results than the Conditional Random Field method as the best-performing baseline. On the other hand, although the results of the NMT approach show a better performance compared to other baseline approaches, it cannot achieve better performance than the Neural Sequence Labeling method. The best achieved F1-measure based on neural sequence labeling is 96.29% Manuscript profile