Enhanced text stemmer with noisy text normalization for Malay texts
In general, the current text stemmers for Malay texts were not developed for text stemming against social media texts. Therefore, there is a need to develop an enhanced text stemmer that is able to map morphological variants based on the characteristics of non-standard derived word patterns on socia...
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Springer, Singapore
2020
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my.utm.930312023-04-04T07:43:53Z http://eprints.utm.my/id/eprint/93031/ Enhanced text stemmer with noisy text normalization for Malay texts Kassim, Mohamad Nizam Mat Jali, Shaiful Hisham Maarof, Mohd. Aizaini Zainal, Anazida Abdul Wahab, Amirudin QA75 Electronic computers. Computer science In general, the current text stemmers for Malay texts were not developed for text stemming against social media texts. Therefore, there is a need to develop an enhanced text stemmer that is able to map morphological variants based on the characteristics of non-standard derived word patterns on social media platforms. It deals with noncompliance word patterns (also called noisy texts or micro text) such as misspelled word and texting language which are often being used as informal conversation. This paper proposes an enhanced text stemmer to perform text stemming against social media texts. The investigation focuses on different patterns of non-standard, non-derived words (mechanics, non-standard word formation, code-switching, and slang words) and also non-standard derived words. The experimental results show that the performance of the proposed text stemmer depends on how much “noise” is in social media texts. Springer, Singapore 2020 Book Section PeerReviewed Kassim, Mohamad Nizam and Mat Jali, Shaiful Hisham and Maarof, Mohd. Aizaini and Zainal, Anazida and Abdul Wahab, Amirudin (2020) Enhanced text stemmer with noisy text normalization for Malay texts. In: Smart Trends in Computing and Communications Proceedings of SmartCom 2019. Smart Innovation, Systems and Technologies, 165 (NA). Springer, Singapore, Gateway East, Singapore, pp. 433-444. ISBN 978-981-15-0076-3 http://dx.doi.org/10.1007%2F978-981-15-0077-0_44 DOI : 10.1007%2F978-981-15-0077-0_44 |
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QA75 Electronic computers. Computer science Kassim, Mohamad Nizam Mat Jali, Shaiful Hisham Maarof, Mohd. Aizaini Zainal, Anazida Abdul Wahab, Amirudin Enhanced text stemmer with noisy text normalization for Malay texts |
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In general, the current text stemmers for Malay texts were not developed for text stemming against social media texts. Therefore, there is a need to develop an enhanced text stemmer that is able to map morphological variants based on the characteristics of non-standard derived word patterns on social media platforms. It deals with noncompliance word patterns (also called noisy texts or micro text) such as misspelled word and texting language which are often being used as informal conversation. This paper proposes an enhanced text stemmer to perform text stemming against social media texts. The investigation focuses on different patterns of non-standard, non-derived words (mechanics, non-standard word formation, code-switching, and slang words) and also non-standard derived words. The experimental results show that the performance of the proposed text stemmer depends on how much “noise” is in social media texts. |
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Book Section |
author |
Kassim, Mohamad Nizam Mat Jali, Shaiful Hisham Maarof, Mohd. Aizaini Zainal, Anazida Abdul Wahab, Amirudin |
author_facet |
Kassim, Mohamad Nizam Mat Jali, Shaiful Hisham Maarof, Mohd. Aizaini Zainal, Anazida Abdul Wahab, Amirudin |
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Kassim, Mohamad Nizam |
title |
Enhanced text stemmer with noisy text normalization for Malay texts |
title_short |
Enhanced text stemmer with noisy text normalization for Malay texts |
title_full |
Enhanced text stemmer with noisy text normalization for Malay texts |
title_fullStr |
Enhanced text stemmer with noisy text normalization for Malay texts |
title_full_unstemmed |
Enhanced text stemmer with noisy text normalization for Malay texts |
title_sort |
enhanced text stemmer with noisy text normalization for malay texts |
publisher |
Springer, Singapore |
publishDate |
2020 |
url |
http://eprints.utm.my/id/eprint/93031/ http://dx.doi.org/10.1007%2F978-981-15-0077-0_44 |
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1762837425847009280 |
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13.18916 |