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Majid Abdolrazzagh-Nezhad
Bozorgmehr University of Qaenat.
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Improving keyword extraction in multilingual texts Bahare Hashemzahde; Majid Abdolrazzagh-Nezhad
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (876.917 KB) | DOI: 10.11591/ijece.v10i6.pp5909-5916

Abstract

The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to that language holds a high rank based on the keywords criteria in other languages, as well. To achieve to this aim, the average TF-IDF of the candidate words were calculated for the same and the other languages. Then the words with the higher averages TF-IDF were chosen as the extracted keywords. The obtained results indicat that the algorithms’ accuracis of the multilingual texts in term frequency-inverse document frequency (TF-IDF) algorithm, graph-based algorithm, and the improved proposed algorithm are 80%, 60.65%, and 91.3%, respectively.
Co-Authors Bahare Hashemzahde