WordNet is a large lexical database of the English language in which nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms (synsets). Each synset expresses a distinct concept. Synsets are interlinked by both semantic and lexical relations. WordNet is essentially used for word sense disambiguation, information retrieval, and text translation. In this paper, we propose several automatic methods to extract Information and Communication Technology (ICT)-related data from Princeton WordNet. We then add these extracted data to our Persian WordNet. The advantage of automated methods is to reduce the interference of human factors and accelerate the development of our bilingual ICT WordNet. In our first proposed method, based on a small subset of ICT words, we use the definition of each synset to decide whether that synset is ICT. The second mechanism is to extract the synsets that are in a semantic relation with the ICT synsets. We also use two similarity criteria, namely LCS and S3M, to measure the similarity between a synset definition in WordNet and definition of any word in Microsoft dictionary. Our last method is to verify the coordinate of ICT synsets. The results obtained show that our proposed mechanisms are able to extract the ICT data from Princeton WordNet at a good level of accuracy.

Awareness of others' opinions plays a crucial role in the decision making process performed by simple customers to top-level executives of manufacturing companies and various organizations. Today, with the advent of Web 2. 0 and the expansion of social networks, a vast number of texts related to people's opinions have been created. However, exploring the enormous amount of documents, various opinion sources and opposing opinions about an entity have made the process of extracting and analyzing opinions very difficult. Hence, there is a need for methods to explore and summarize the existing opinions. Accordingly, there has recently been a new trend in natural language processing science called "opinion mining". The main purpose of opinion mining is to extract and detect people’ s positive or negative sentiments (sense of satisfaction) from text reviews. The absence of a comprehensive Persian sentiment lexicon is one of the main challenges of opinion mining in Persian. In this paper, a new methodology for developing Persian Sentiment WordNet (HesNegar) is presented using various Persian and English resources. A corpus of Persian reviews developed for opinion mining studies are introduced. To develop HesNegar, a comprehensive Persian WordNet (FERDOWSNET), with high recall and proper precision (based on Princeton WordNet), was first created. Then, the polarity of each synset in English SentiWordNet is mapped to the corresponding words in HesNegar. In the conducted tests, it was found that HesNegar has a precision score of 0. 86 a recall score of 0. 75 and it can be used as a comprehensive Persian SentiWordNet. The findings and developments made in this study could prove useful in the advancement of opinion mining research in Persian and other similar languages, such as Urdu and Arabic.

This paper presents an automated supervised method for Persian wordnet construction. Using a Persian corpus and a bi-lingual dictionary, the initial links between Persian words and Princeton WordNet synsets have been generated. These links will be discriminated later as correct or incorrect by employing seven features in a trained classification system. The whole method is just a classification system which has been trained on a train set containing a pre-existing Persian wordnet, FarsNet, as a set of correct instances. A set of some sophisticated distributional and semantic features is proposed to be used in the classification system. Furthermore, a set of randomly selected links have been added to training data as incorrect instances. The links classified as correct are collected to be included in the final wordnet. State of the art results on the automatically derived Persian wordnet is achieved. The resulted wordnet with a precision of 91. 18% includes more than 16, 000 words and 22, 000 synsets.

The growing importance of electronic tools for compiling, storing, and processing the linguistic data made us build a WordNet for Persian adjectives. This paper starts with a brief introduction on the history and general concepts of WordNet. To arrive at a semantic classification of Persian adjectives, we categorized Persian adjectives in 15 main classes and over 70 minor groups. Three Persian dictionaries and an electronic corpus have been employed for extracting the entries and identifying their semantic fields. Since WordNet is a kind of database, we have introduced some key notions and methodology of databases architecture.

This paper passes under review the history of WordNets and introduces some of the most prominent projects of Word Net construction in order to illustrate how these tools can be employed as lexicon in finding equivalents in machine translation projects. Then the process of designing and developing the Persian noun WordNet-as a part of the Persian WordNet which is an important tool for processing Persian language-is discussed.At the end, the applications of Word Nets in general and more specifically their application in machine translation are put forward.

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.

Shortly after gulf war (G.W) thousands of war veterans referred with sign and symptoms of an unknown disease called G.W illness. Despite multiple studies undertaken by several non-governmental and governmental centers this illness remains unclassified and can not be coded by international classification of disease (I.C.D). The etiology has not been found and the illness appears to be multifactorial and basically related to use of depleted uranium (D.U) weapons, chemical warfare (C.W) and biological agents. This article reviews this illness and the adverse effects of D.U and B.c.W agents in the G.W battlefields.

In this research the difficulties in the mapping of Farsnet synsets with the Princeton WordNet synsets were investigated. Â Regarding the three kinds of difficulties in mapping of synsets between the WordNets including 1. Difficulties related to the meaning distinction in the source WordNet 2. Difficulties related to the principles underlying the source WordNet and the target language resources and 3. Difficulties related to the intrinsic differences between the source and target languages, the synsets and their mappings were investigated. This research tried to answer three questions: What are the difficulties in the mapping Farsnet synsets with Princeton Synsets? Which difficulties were more frequent? Was there any difference between the difficulties in the mapping of Farsnet synsets and Princeton WordNet and mapping of synsets of other WordNets? Considering the large amount of the data a sample of 1552 synsets were chosen randomly. With regard to the overlap of words between synsets, only the first member of the synset was taken into account. The cases were divided into eight types. For solving the observed difficulties some suggestions were proposed that can be used for Farsnet enrichment and in designing and developing other WordNets for special disciplines.

Easy access to social media enables users to express their opinions and ideology about various topics like news, videos, and personalities freely, without any fear, and often in an offensive manner. It is a vital task to detect comments with offensive language on social media platforms and relies on a complete and comprehensive tagged dataset. Therefore, in this paper, we introduce and make publicly available PerBOLD, a new Persian comment dataset collected from Instagram as a popular platform among Iranian. We follow a two-level manual annotation process in order to determine whether a comment has offensive language or not and fine-grained tags of different types of offensive language. Furthermore, we present some interesting aspects of data and analysis them.

This paper discusses the process of designing and developing the Persian noun Wordnet as an important tool for natural language processing. First, an introduction to the most significant projects for building WordNets around the world and their prominent features and applications is provided. Then different stages of designing and developing of Persian noun WordNet including editor, building process and language resources will be discussed. At the end, we will show the capability of connecting the developed noun Wordnet to other Persian WordNets (adjective and verb WordNets) and the WordNets of other languages.