AN APPROACH FOR EXTRACTION OF KEYWORDS AND WEIGHTING WORDS FOR IMPROVEMENT FARSI DOCUMENTS CLASSIFICATION

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Rezaei Vahideh; MOHAMMADPOUR MAJID; PARVIN HAMID; NEJATIAN SAMAD

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Journal: SIGNAL AND DATA PROCESSING Year:2018 | Volume:14 | Issue:4 (SERIAL 34) Page(s): 55-78

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Title

AN APPROACH FOR EXTRACTION OF KEYWORDS AND WEIGHTING WORDS FOR IMPROVEMENT FARSI DOCUMENTS CLASSIFICATION

Author(s)

Rezaei Vahideh | MOHAMMADPOUR MAJID | PARVIN HAMID | NEJATIAN SAMAD | Issue Writer Certificate

Keywords

THESAURUSQ2

INFORMATION RETRIEVALQ1

EXTRACTION OF KEYWORDSQ1

WEIGHTQ1

Abstract

Due to ever-increasing information expansion and existing huge amount of unstructured documents, usage of keywords plays a very important role in INFORMATION RETRIEVAL. Because of a manually-extraction of keywords faces various challenges, their automated extraction seems inevitable. In this research, it has been tried to use a THESAURUS, (a structured word-net) to automatically extract them. Authors claim that extraction of more meaningful keywords out of documents can be attained via employment of a THESAURUS. The keywords extracted by applying THESAURUS, can improve the document classification. The steps to be taken to increase the comprehensiveness of search should be such that in the first step the stop words are removed and the remaining words are stemmed. Then, with the help of a THESAURUS are found words equivalent, hierarchical and dependent. Then, to determine the relative importance of words, a numerical WEIGHT is assigned to each word, which represents effect of the word on the subject matter and in comparison with other words used in the text. According to the steps above and with the help of a THESAURUS, an accurate text classification is performed. In this method, the KNN algorithm is used for the classification. Due to the simplicity and effectiveness of this algorithm (KNN), there is a great deal of use in the classification of texts. The cornerstone of KNN is to compare with the text trained and text tested to determine their similarity between. The empirical results show the quality and accuracy of extracted keywords are satisfiable for users. They also confirm that the document classification has been enhanced. In this research, it has been tried to extract more meaningful keywords out of texts using THESAURUS (which is a structured word-net) rather than not using it.

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APA: Copy

Rezaei, Vahideh, MOHAMMADPOUR, MAJID, PARVIN, HAMID, & NEJATIAN, SAMAD. (2018). AN APPROACH FOR EXTRACTION OF KEYWORDS AND WEIGHTING WORDS FOR IMPROVEMENT FARSI DOCUMENTS CLASSIFICATION. SIGNAL AND DATA PROCESSING, 14(4 (SERIAL 34) ), 55-78. SID. https://sid.ir/paper/160830/en

Vancouver: Copy

Rezaei Vahideh, MOHAMMADPOUR MAJID, PARVIN HAMID, NEJATIAN SAMAD. AN APPROACH FOR EXTRACTION OF KEYWORDS AND WEIGHTING WORDS FOR IMPROVEMENT FARSI DOCUMENTS CLASSIFICATION. SIGNAL AND DATA PROCESSING[Internet]. 2018;14(4 (SERIAL 34) ):55-78. Available from: https://sid.ir/paper/160830/en

IEEE: Copy

Vahideh Rezaei, MAJID MOHAMMADPOUR, HAMID PARVIN, and SAMAD NEJATIAN, “AN APPROACH FOR EXTRACTION OF KEYWORDS AND WEIGHTING WORDS FOR IMPROVEMENT FARSI DOCUMENTS CLASSIFICATION,” SIGNAL AND DATA PROCESSING, vol. 14, no. 4 (SERIAL 34) , pp. 55–78, 2018, [Online]. Available: https://sid.ir/paper/160830/en

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