One of the most challenging and difficult aspects of translation is related to the translation of idioms by both machine systems and human translators. The problem arises from the recognition of the idioms on the one hand and translating them on the other hand. idiom recognition is not easy since an idiom may change its form based on the context in which it is used. Using systematic idiom collections and special rules to recognize idioms, the problem can be solved to a great extent. Moreover, the inclusion of a morphological as well as syntactic parser can greatly enhance the idiom recognition and translation process. The aim of this paper is twofold. First, we want to test Google Translate to see how well it is capable of translating idioms and next to provide means to improve the quality of translation of idioms by Google Translate.

The main objective of this study was to investigate whether searching firm's ticker symbol and name in Google can predict its future stock market activities. In so doing, the data related to firms' ticker symbol and firms' name were collected using Google Trend and stock market activity was measured using four proxies, namely abnormal return, return volatility, stock trading volume and stock trading count. In order to meet the main objective of the study, multiple regression and panel regression were used over13082 firmmonth observation during the years between 2005 and 2018. The results showed that the future market activity, including return volatility, stock trading volume and stock trading count, increased with searching the firms' ticker and name in Google. However, there was no significant relationship between the future abnormal return and Google searches. Findings also showed that future market activity can be predicted using Google searches. In addition, there was a more significant relationship between the searched firms' ticker symbol than the searched firms' name and future market activity.

The memetic mapping is a method to identify memes and categorize them in the form of a multilevel model. Meme is a reproducible cultural element and has been adapted from the concept of gene in the evolutionary biology. The purpose of this study was to describe the current culture of Google using memetic mapping. To this end, the printed and online texts published about Google were coded for memes. This way, 134 memes were extracted and reduced to 40 Memecules (a higher unit comprised of several related memes). Memecules were placed into 6 levels of organizational culture, namely fundamental assumptions, values, norms, artifacts, work procedures, and human resources procedures. Then, in order to understand the formation of Google’ s culture in the course of history, cultural genealogy method was applied. In this approach, the father of each meme (the culture from which the meme is adopted) is identified, and then the children are connected to their parents with an arrow. The outcome is a cultural genealogy model in shape of a family tree graph. In this study, 85 Greek and Indian cultures with impacts on the emergence of Google’ s culture were recognized and analyzed. This study successfully operationalized the meme concept and employed it in an empirical research. Memetic mapping is a powerful tool available for managers, consultants, and organizational researchers to explore the cultural phenomenon and to change it. Memetic engineering is capable of diagnosing and correcting a defective meme in a human community.

Google Translator Toolkit is one of the recent online translation tools made available to professional and non- professional users. This toolkit is an editing tool which enables users to edit the translations provided. by Google Translate. Google Translator Toolkit has several features which can make it interesting and at the same time useful for translators. First of all, it is rather easy to work. Second, its user- friendly interface allows users to easily edit the translations done by Google Translate, while at the same time provides access to existing translations done by other users. Last but not least, multiple users can work on a document and collaboratively translate it at the same time. The present paper elaborates on an observational study into the use of Google Translator Toolkit by a class of senior student translators at Allameh Tabataba'i University, Tehran, Iran in the context of translation of political texts from English into Persian. The results are drawn from the teacher's observation and the students' class discussions along with their answers to a questionnaire on the use of Google Translator Toolkit.

The aim off this research was to investigate user's query expansion in Google search Engine based on the "Cognitive Load Theory" and the extent and types of cognitive loads resulted from suggested keywords/queries presented in Google search engine. In this article, data were collected from 60 postgraduate students in Humanities/Social Sciences and Basic and Applied Sciences - at Ferdowsi University of Mashhad, Iran. Non- probability sampling - especially purposive sampling- was used as the sampling method. Also, individual sessions were administered with each of the students. Findings show that suggested keywords/queries and the retrieved pages do not make any significant problem (or negative cognitive load) for users. Also, there is no significant difference between students of the two fields of study regarding cognitive load in the query expansion process in Google. Approximately most of the suggested keywords/queries and the retrieved pages matched with users' needs and had direct relationship with their initial keywords. On the whole, the suggested keywords and queries have taken an appropriate approach regading more relevant results in this search engine.

Purpose: This study examines the effect of Google's local search on information asymmetry before earnings announcement and short-term and long-term market reactions after earnings announcement. Method: Google's local biases are measured by Google Trend service information. Also, to measure information asymmetry and market response, abnormal stock returns in the periods before and after the announcement of earnings are used. The research sample includes 64 companies listed on the Tehran Stock Exchange for the years 1393 to 1398. Results: The results of the study indicate that local bias increases the information asymmetry before the announcement of profits. However, the impact of local bias on market response is limited to a short period after the announcement of profits. Conclusion: Overall, the evidence suggests that local biases resulting from investors' attention to companies close to their place of residence affect information asymmetries and short-term market response. Contribution: This study shows for the first time that even in the age of the Internet and the ease of access to information of companies, the geographical location of companies influences the decisions of Iranian investors and the stock market.