Finding the Relevance Degree between an English Text and its Title
Keywords are useful tools as they give the shorter summary of the document. Keywords are useful for a variety of purposes including summarizing, indexing, labeling, categorization, clustering, and searching, and in this paper we will use keywords in order to find the relevance degree between an Engl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2012-05-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_56940_879338811605983c9dfc0f5f97f609fb.pdf |
| _version_ | 1827358406380355584 |
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| author | Abdul Monem S. Rahma Suhad M. Kadhem Alaa Kadhim Farhan |
| author_facet | Abdul Monem S. Rahma Suhad M. Kadhem Alaa Kadhim Farhan |
| author_sort | Abdul Monem S. Rahma |
| collection | DOAJ |
| description | Keywords are useful tools as they give the shorter summary of the document. Keywords are useful for a variety of purposes including summarizing, indexing, labeling, categorization, clustering, and searching, and in this paper we will use keywords in order to find the relevance degree between an English text and its title. The proposed system solves this problem through simple statistic (Term frequency) and linguistic approaches by extracting the keywords of the title and keywords of the text (with their frequency that appear in the text) and finding the average of title's keywords frequency across the text that represent the relevance degree that required, with depending on a lexicon of a particular field(in this work we choose computer science field). This lexicon is represented using two different B+ trees one for non-keywords and the other for candidate keywords, these keywords was stored in a manner that prevent redundancy of these terms or even sub-terms to provide efficient memory usage and to minimize the search time. The proposed system was implemented using Visual Prolog 5.1 and after testing, it proved to be valuable for finding the degree of relevance between a text and its title (from point of view of accuracy and search time). |
| first_indexed | 2024-03-08T06:10:19Z |
| format | Article |
| id | doaj.art-3f979457447940d7b82c6c2a0a38ac22 |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T06:10:19Z |
| publishDate | 2012-05-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-3f979457447940d7b82c6c2a0a38ac222024-02-04T17:39:18ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582012-05-013091625164010.30684/etj.30.9.1456940Finding the Relevance Degree between an English Text and its TitleAbdul Monem S. RahmaSuhad M. KadhemAlaa Kadhim FarhanKeywords are useful tools as they give the shorter summary of the document. Keywords are useful for a variety of purposes including summarizing, indexing, labeling, categorization, clustering, and searching, and in this paper we will use keywords in order to find the relevance degree between an English text and its title. The proposed system solves this problem through simple statistic (Term frequency) and linguistic approaches by extracting the keywords of the title and keywords of the text (with their frequency that appear in the text) and finding the average of title's keywords frequency across the text that represent the relevance degree that required, with depending on a lexicon of a particular field(in this work we choose computer science field). This lexicon is represented using two different B+ trees one for non-keywords and the other for candidate keywords, these keywords was stored in a manner that prevent redundancy of these terms or even sub-terms to provide efficient memory usage and to minimize the search time. The proposed system was implemented using Visual Prolog 5.1 and after testing, it proved to be valuable for finding the degree of relevance between a text and its title (from point of view of accuracy and search time).https://etj.uotechnology.edu.iq/article_56940_879338811605983c9dfc0f5f97f609fb.pdfkeyword extractionlexiconmorphologytree |
| spellingShingle | Abdul Monem S. Rahma Suhad M. Kadhem Alaa Kadhim Farhan Finding the Relevance Degree between an English Text and its Title Engineering and Technology Journal keyword extraction lexicon morphology tree |
| title | Finding the Relevance Degree between an English Text and its Title |
| title_full | Finding the Relevance Degree between an English Text and its Title |
| title_fullStr | Finding the Relevance Degree between an English Text and its Title |
| title_full_unstemmed | Finding the Relevance Degree between an English Text and its Title |
| title_short | Finding the Relevance Degree between an English Text and its Title |
| title_sort | finding the relevance degree between an english text and its title |
| topic | keyword extraction lexicon morphology tree |
| url | https://etj.uotechnology.edu.iq/article_56940_879338811605983c9dfc0f5f97f609fb.pdf |
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