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Book ChapterDOI

Neologisms in Contemporary Persian Approved by the Academy of Persian Language and Literature: A Case Study of Epidemiology Terms

01 Jan 2021-pp 81-103
TL;DR: In this paper, the authors identify ten terms in the field of epidemiology related to the outbreak of the Corona pandemic in 2020 and classify them into three groups: (1) terms that have no previously existing equivalent approved by the Academy of Persian Language and Literature (APLL), (2) terms for which the APLL has approved Persian equivalents but which are still in use in parallel with foreign ones.
Abstract: Amid the openness we witness in the world, it is difficult to control the mixing of foreign terms and loanwords that enter into the vocabulary of other languages – be it Arabic, Persian, or French. However, some countries are still setting out to codify the use of foreign terms and maintain their language and national identity. As such we find the Academy of Persian Language and Literature (APLL) in Iran with its attempt to preserve Persian identity, culture, civilization, and heritage. While its original goal was and still is to maintain the strength and originality of the Persian language, this task has become harder with the influx of new words from across all disciplines of science. Methodologically, this paper is based on a corpus analysis using the software Sketch Engine. The corpus contains texts from the online archives of numerous Persian-language Iranian newspapers. To shed light on foreign terms and their Persian equivalents this paper identifies ten terms in the field of epidemiology related to the outbreak of the Corona pandemic in 2020. The shortlisted terms can be categorized into three different groups: (1) terms that have no previously existing equivalent approved by the APLL, (2) terms that have been accepted and approved by the APLL for their prevalence in popular usage, and (3) terms for which the APLL has approved Persian equivalents but which are still in use in parallel with foreign ones. The ten epidemiology-related terms in this case study can be distributed among the three categories as follows: two in group 1, three in group 2, and five terms in group 3. Two examples of group 3 terms will be given compared to just one each from groups 1 and 2. The group 3 terms facilitate a direct comparison between approved and non-approved terms and are therefore especially relevant in the context of this study. This not only reveals the mixed success of APLL approved equivalents, but it shows more generally how the APLL has created new terms or reused existing terms and how the APLL carries out its tasks in the past, present, and future.
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Book ChapterDOI
17 Aug 2018
TL;DR: This paper uses the information entropy and relative entropy in information theory as the calculation factor, adds to the above improved TFIDF algorithm, optimizes its performance, and passes Simulation experiments verify its performance.
Abstract: With the development of information technology and the increasing richness of network information, people can more and more easily search for and obtain the required information from the network. However, how to quickly obtain the required information in the massive network information is very important. Therefore, information retrieval technology emerges, One of the important supporting technologies is keyword extraction technology. Currently, the most widely used keyword extraction technique is the TF-IDFs algorithm (Term Frequency-Inverse Document Frequency). The basic principle of the TF-IDF algorithm is to calculate the number of occurrences of words and the frequency of words. It ranks and selects the top few words as keywords. The TF-IDF algorithm has features such as simplicity and high reliability, but there are also deficiencies. This paper analyzes its shortcomings for an improved TFIDF algorithm, and optimizes it from the information theory point of view. It uses the information entropy and relative entropy in information theory as the calculation factor, adds to the above improved TFIDF algorithm, optimizes its performance, and passes Simulation experiments verify its performance.

4 citations