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Nowadays the amount of textual information on the web is grown rapidly. The huge textual data needs more accurate classification algorithms. Sentiment analysis is a branch of text classification that is used to classify user opinions in case of market decisions, product evaluations or measuring consumer confidence. With the rise of the production rate of Persian text data in a commercial area, improvement of the efficiency of algorithms in Persian is a must. The structure of the Persian language such as word and sentence structures poses some challenges in this area. Deep learning algorithms are recently used in NLP and especially sentiment text classification for many dominant languages like Persian. The goal is to improve the performance of classification using deep learning issues. In this work, the authors proposed a hybrid method by a combination of structural correspondence learning (SCL) and convolutional neural network (CNN). The SCL method selects the most effective pivot features so the adaptation from one domain to similar ones cannot drop the efficiency drastically. The results showed that the proposed hybrid method that is learned from one domain can act efficiently in a similar domain. The result showed that applying a combination of SCL+CNN can improve the result of sentiment classification for two domains more than 10 percent. [ABSTRACT FROM AUTHOR]

Heterogeneous data in all groups are growing on the web nowadays. Because of the variety of data types in the web search results, it is common to classify the results in order to find the preferred data. Many machine learning methods are used to classify textual data. The main challenges in data classification are the cost of classifier and performance of classification. A traditional model in IR and text data representation is the vector space model. In this representation cost of computations are dependent upon the dimension of the vector. Another problem is to select effective features and prune unwanted terms. Latent semantic indexing is used to transform VSM to orthogonal semantic space with term relation consideration. Experimental results showed that LSI semantic space can achieve better performance in computation time and classification accuracy. This result showed that semantic topic space has less noise so the accuracy will increase. Less vector dimension also reduces the computational complexity. [ABSTRACT FROM AUTHOR]

Due to the special features of Persian, developing natural language processing tools for it involves an array of challenges. Lack of efficient Persian knowledge sources is another obstacle to research this language. The goal of this article was to overcome these problems by implementing spelling correction task. The main outputs of this study included a parallel corpus, an N-gram language model for Persian, and a semantic-based spelling correction system named Perspell, which made use of extracted language model. Compared to its rival software (including Vafa spellchecker), Perspell could detect and correct nonword and real word errors more successfully. The rate of real word error detection in Perspell was 95%. In fact, its outstanding ability to detect real word errors as well as its significant improvement in terms of F-measure were the two advantages of the proposed system. [ABSTRACT FROM AUTHOR]

The Digital Object Recognizer (DOR) is a system for identifying Persian digital objects on the web. DOR is a unique alphanumeric string assigned by Regional Information Center for Science and Technology (RICeST) to any entity to identify content and provide a persistent link to its location on the Internet digital networks. The paper describes the construction and functions of the DOR system for Persian articles and then introduces and outlines the underlying architecture regarding DOR system. The study concluded that using DOR will enable Iranian journal publishers assign it to both print and electronic journal articles; finally enumerated the benefits of DOI for stakeholders in the scholarly communication process. [ABSTRACT FROM AUTHOR]

A bilingual corpus is considered as a very important knowledge source and an inevitable requirement for many natural language processing (NLP) applications in which two languages are involved. For some languages such as Persian, lack of such resources is much more significant. Several applications, including statistical and example-based machine translation needs bilingual corpora, in which large amounts of texts from two different languages have been aligned at the sentence or phrase levels. In order to meet this requirement, this paper aims to propose an accurate and hybrid sentence alignment method for construction of an English- Persian parallel corpus. As the first step, the proposed method uses statistical length based analysis for filtering of candidates. Punctuation marks are used as a directing feature to reduce the complexity and increase the accuracy. Finally, the proposed method makes use of some lexical knowledge in order to produce the final output. . In the phase of lexical analysis, a bilingual dictionary as well as a Persian semantic net (denoted as FarsNet) is used to calculate the extended semantic similarity. Experiments showed the positive effect of expansion on synonym words by extended semantic similarity on the accuracy of the sentence alignment process. In the proposed matching scheme, a semantic load based approach (which considers the verb as the pivot and the main part of a sentence) was also used in order for increasing the accuracy. The results obtained from the experiments were promising and the generated parallel corpus can be used as an effective knowledge source by researchers who work on Persian language. [ABSTRACT FROM AUTHOR]