Indexing Arabic texts using association rule data mining
Purpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an assoc...
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2019
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| Online Access: | http://hdl.handle.net/10725/10223 https://doi.org/10.1108/LHT-07-2017-0147 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.emeraldinsight.com/doi/full/10.1108/LHT-07-2017-0147 |
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| _version_ | 1864513486212562944 |
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| author | Haraty, Ramzi A. |
| author2 | Nasrallah, Rouba |
| author2_role | author |
| author_facet | Haraty, Ramzi A. Nasrallah, Rouba |
| author_role | author |
| dc.creator.none.fl_str_mv | Haraty, Ramzi A. Nasrallah, Rouba |
| dc.date.none.fl_str_mv | 2019-03-15T13:22:42Z 2019-03-15T13:22:42Z 2019 2019-03-15 |
| dc.identifier.none.fl_str_mv | 0737-8831 http://hdl.handle.net/10725/10223 https://doi.org/10.1108/LHT-07-2017-0147 Haraty, R. A., & Nasrallah, R. (2019). Indexing Arabic texts using association rule data mining. Library Hi Tech, 37(1), 101-117. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.emeraldinsight.com/doi/full/10.1108/LHT-07-2017-0147 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Library Hi Tech |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Indexing Arabic texts using association rule data mining |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Purpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together. Findings The proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works. Research limitations/implications The stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results. Originality/value In this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_bc57801b5d87d0f2c4b60d16bb62f97f |
| identifier_str_mv | 0737-8831 Haraty, R. A., & Nasrallah, R. (2019). Indexing Arabic texts using association rule data mining. Library Hi Tech, 37(1), 101-117. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/10223 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Indexing Arabic texts using association rule data miningHaraty, Ramzi A.Nasrallah, RoubaPurpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together. Findings The proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works. Research limitations/implications The stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results. Originality/value In this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts.PublishedN/A2019-03-15T13:22:42Z2019-03-15T13:22:42Z20192019-03-15Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0737-8831http://hdl.handle.net/10725/10223https://doi.org/10.1108/LHT-07-2017-0147Haraty, R. A., & Nasrallah, R. (2019). Indexing Arabic texts using association rule data mining. Library Hi Tech, 37(1), 101-117.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://www.emeraldinsight.com/doi/full/10.1108/LHT-07-2017-0147enLibrary Hi Techinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/102232021-03-19T10:45:29Z |
| spellingShingle | Indexing Arabic texts using association rule data mining Haraty, Ramzi A. |
| status_str | publishedVersion |
| title | Indexing Arabic texts using association rule data mining |
| title_full | Indexing Arabic texts using association rule data mining |
| title_fullStr | Indexing Arabic texts using association rule data mining |
| title_full_unstemmed | Indexing Arabic texts using association rule data mining |
| title_short | Indexing Arabic texts using association rule data mining |
| title_sort | Indexing Arabic texts using association rule data mining |
| url | http://hdl.handle.net/10725/10223 https://doi.org/10.1108/LHT-07-2017-0147 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.emeraldinsight.com/doi/full/10.1108/LHT-07-2017-0147 |