On the Use of Supervised Learning Method for Authorship Attribution
In this paper we investigate the use of a supervised learning method for the authorship attribution that is for the identification of the author of a text. We suggest a new, simple and efficient method, which is merely based on counting the number of repetitions of each alphabetic letter in the text...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Unviversity of Technology- Iraq
2012-01-01
|
| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_25844_8e7cbca45b7ee3810093c7630d664166.pdf |
| _version_ | 1797325574695288832 |
|---|---|
| author | Walaa M. Khalaf |
| author_facet | Walaa M. Khalaf |
| author_sort | Walaa M. Khalaf |
| collection | DOAJ |
| description | In this paper we investigate the use of a supervised learning method for the authorship attribution that is for the identification of the author of a text. We suggest a new, simple and efficient method, which is merely based on counting the number of repetitions of each alphabetic letter in the text, instead of using the traditional classification properties; such as the contents of the text and style of the author; which falls into four feature categories: lexical, syntactic, structural, and content-specific. Furthermore, we apply a spherical classification method. We apply the proposed technique to the work of two Italian writers, Dante Alighieri and Brunetto Latini. With almost high reliability, the spherical classifier proved its ability to discriminate between the selected authors. Finally the results are compared with those obtained by means of a standard Support Vector Machine classifier. |
| first_indexed | 2024-03-08T06:11:23Z |
| format | Article |
| id | doaj.art-5f5add9d335e4ce48e99e383cd44601d |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T06:11:23Z |
| publishDate | 2012-01-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-5f5add9d335e4ce48e99e383cd44601d2024-02-04T17:38:18ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582012-01-0130228229210.30684/etj.30.2.825844On the Use of Supervised Learning Method for Authorship AttributionWalaa M. KhalafIn this paper we investigate the use of a supervised learning method for the authorship attribution that is for the identification of the author of a text. We suggest a new, simple and efficient method, which is merely based on counting the number of repetitions of each alphabetic letter in the text, instead of using the traditional classification properties; such as the contents of the text and style of the author; which falls into four feature categories: lexical, syntactic, structural, and content-specific. Furthermore, we apply a spherical classification method. We apply the proposed technique to the work of two Italian writers, Dante Alighieri and Brunetto Latini. With almost high reliability, the spherical classifier proved its ability to discriminate between the selected authors. Finally the results are compared with those obtained by means of a standard Support Vector Machine classifier.https://etj.uotechnology.edu.iq/article_25844_8e7cbca45b7ee3810093c7630d664166.pdfauthorship attributionspherical classificationsupport vector machine |
| spellingShingle | Walaa M. Khalaf On the Use of Supervised Learning Method for Authorship Attribution Engineering and Technology Journal authorship attribution spherical classification support vector machine |
| title | On the Use of Supervised Learning Method for Authorship Attribution |
| title_full | On the Use of Supervised Learning Method for Authorship Attribution |
| title_fullStr | On the Use of Supervised Learning Method for Authorship Attribution |
| title_full_unstemmed | On the Use of Supervised Learning Method for Authorship Attribution |
| title_short | On the Use of Supervised Learning Method for Authorship Attribution |
| title_sort | on the use of supervised learning method for authorship attribution |
| topic | authorship attribution spherical classification support vector machine |
| url | https://etj.uotechnology.edu.iq/article_25844_8e7cbca45b7ee3810093c7630d664166.pdf |
| work_keys_str_mv | AT walaamkhalaf ontheuseofsupervisedlearningmethodforauthorshipattribution |