Measuring Short Text Reuse for the Urdu Language
Text reuse occurs when one borrows the text (either verbatim or paraphrased) from an earlier written text. A large and increasing amount of digital text is easily and readily available, making it simpler to reuse but difficult to detect. As a result, automatic detection of text reuse has attracted t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8118088/ |
| _version_ | 1818331693917929472 |
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| author | Sara Sameen Muhammad Sharjeel Rao Muhammad Adeel Nawab Paul Rayson Iqra Muneer |
| author_facet | Sara Sameen Muhammad Sharjeel Rao Muhammad Adeel Nawab Paul Rayson Iqra Muneer |
| author_sort | Sara Sameen |
| collection | DOAJ |
| description | Text reuse occurs when one borrows the text (either verbatim or paraphrased) from an earlier written text. A large and increasing amount of digital text is easily and readily available, making it simpler to reuse but difficult to detect. As a result, automatic detection of text reuse has attracted the attention of the research community due to the wide variety of applications associated with it. To develop and evaluate automatic methods for text reuse detection, standard evaluation resources are required. In this paper, we propose one such resource for a significantly under-resourced language-Urdu, which is widely used in day to day communication and has a large digital footprint particularly in the Indian subcontinent. Our proposed Urdu short text reuse corpus contains 2684 short Urdu text pairs, manually labeled as verbatim (496), paraphrased (1329), and independently written (859). In addition, we describe an evaluation of the corpus using various state-of-the-art text reuse detection methods with binary and multi-classification settings and a set of four classifiers. Output results show that character n-gram overlap using J48 classifier outperform other methods for the Urdu short text reuse detection task. |
| first_indexed | 2024-12-13T13:23:55Z |
| format | Article |
| id | doaj.art-47bd7af9d3314083a28218e3aeee2200 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:23:55Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-47bd7af9d3314083a28218e3aeee22002022-12-21T23:44:21ZengIEEEIEEE Access2169-35362018-01-0167412742110.1109/ACCESS.2017.27768428118088Measuring Short Text Reuse for the Urdu LanguageSara Sameen0Muhammad Sharjeel1https://orcid.org/0000-0003-3361-4335Rao Muhammad Adeel Nawab2Paul Rayson3Iqra Muneer4Department of Examinations, Virtual University of Pakistan, Lahore, PakistanSchool of Computing and Communications, Lancaster University, Lancaster, U.K.Department of Computer Science, COMSATS Institute of Information Technology, Lahore, PakistanSchool of Computing and Communications, Lancaster University, Lancaster, U.K.Department of Computer Science, Rachna College of Engineering and Technology, Gujranwala, PakistanText reuse occurs when one borrows the text (either verbatim or paraphrased) from an earlier written text. A large and increasing amount of digital text is easily and readily available, making it simpler to reuse but difficult to detect. As a result, automatic detection of text reuse has attracted the attention of the research community due to the wide variety of applications associated with it. To develop and evaluate automatic methods for text reuse detection, standard evaluation resources are required. In this paper, we propose one such resource for a significantly under-resourced language-Urdu, which is widely used in day to day communication and has a large digital footprint particularly in the Indian subcontinent. Our proposed Urdu short text reuse corpus contains 2684 short Urdu text pairs, manually labeled as verbatim (496), paraphrased (1329), and independently written (859). In addition, we describe an evaluation of the corpus using various state-of-the-art text reuse detection methods with binary and multi-classification settings and a set of four classifiers. Output results show that character n-gram overlap using J48 classifier outperform other methods for the Urdu short text reuse detection task.https://ieeexplore.ieee.org/document/8118088/Urdu text reuse detectionUrdu corpusnatural language processing |
| spellingShingle | Sara Sameen Muhammad Sharjeel Rao Muhammad Adeel Nawab Paul Rayson Iqra Muneer Measuring Short Text Reuse for the Urdu Language IEEE Access Urdu text reuse detection Urdu corpus natural language processing |
| title | Measuring Short Text Reuse for the Urdu Language |
| title_full | Measuring Short Text Reuse for the Urdu Language |
| title_fullStr | Measuring Short Text Reuse for the Urdu Language |
| title_full_unstemmed | Measuring Short Text Reuse for the Urdu Language |
| title_short | Measuring Short Text Reuse for the Urdu Language |
| title_sort | measuring short text reuse for the urdu language |
| topic | Urdu text reuse detection Urdu corpus natural language processing |
| url | https://ieeexplore.ieee.org/document/8118088/ |
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