Academic literature on the topic 'Arabic Light stemmer'

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Journal articles on the topic "Arabic Light stemmer"

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Saoudi, Ouahiba, and Roslina Othman. "RETRIEVAL PERFORMANCE OF ARABIC LIGHT STEMMERS." International Journal of Modern Trends in Social Sciences 2, no. 10 (2019): 81–90. http://dx.doi.org/10.35631/ijmtss.210008.

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Despite the fact that stemming greatly improves Arabic information retrieval performance, yet no standard stemmer emerges in the field of Arabic IR due to some limitations and shortcomings. Among the recurring problems is that the stemmer can reduce unrelated words to the same stem as well as fall short to reduce related words to a common stem. Many studies have suggested Arabic algorithms to address the problem associated with stemming. This paper aims to review the state of the retrieval performance of Arabic Light stemmers based on the main objectives achieved, causes for retrieval success
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Abainia, Kheireddine, Siham Ouamour, and Halim Sayoud. "A novel robust Arabic light stemmer." Journal of Experimental & Theoretical Artificial Intelligence 29, no. 3 (2016): 557–73. http://dx.doi.org/10.1080/0952813x.2016.1212100.

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Althunibat, Ahmad, Bayan Alsawareah, Siti Sarah Maidin, et al. "Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms." International Journal of Cloud Applications and Computing 14, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijcac.339563.

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The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success of software development projects. Traditionally, engineers have used manual methods to evaluate requirement quality, leading to a time-consuming and subjective process that is prone to errors. This study explores the use of machine learning algorithms to automate the assessment of requirements expressed in natural language. The study aims to compare various machine learning algorithms according to th
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Namly, Driss, and Karim Bouzoubaa. "An innovative Arabic light stemmer developed using a hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2356. https://doi.org/10.11591/ijece.v15i2.pp2356-2363.

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Our study introduces an innovative light stemming tool tailored for Arabic morphology challenges. In conformance with the templatic and concatenative structures, our stemmer utilizes a combination of clitic stripping, lexicon-based, and statistical disambiguation techniques to ensure accurate stemming. To accomplish this, we rely on our clitic rules lexicon to detect all potential combinations of clitics for each input entry. Subsequently, we depend on an extensive lexicon of over 7 million stems to verify the potential stems. Lastly, we employ a statistical model to ascertain the most likely
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Namly, Driss, and Karim Bouzoubaa. "An innovative Arabic light stemmer developed using a hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2356–63. https://doi.org/10.11591/ijece.v15i2.pp2356-2363.

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Our study introduces an innovative light stemming tool tailored for Arabic morphology challenges. In conformance with the templatic and concatenative structures, our stemmer utilizes a combination of clitic stripping, lexicon- based, and statistical disambiguation techniques to ensure accurate stemming. To accomplish this, we rely on our clitic rules lexicon to detect all potential combinations of clitics for each input entry. Subsequently, we depend on an extensive lexicon of over 7 million stems to verify the potential stems. Lastly, we employ a statistical model to ascertain the most likely
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El-Beltagy, Samhaa R., and Ahmed Rafea. "An accuracy-enhanced light stemmer for arabic text." ACM Transactions on Speech and Language Processing 7, no. 2 (2011): 1–22. http://dx.doi.org/10.1145/1921656.1921657.

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M. Al-Khatib, Ra’ed, Taha Zerrouki, Mohammed M. Abu Shquier, Amar Balla, and Asef Al-Khateeb. "A New Enhanced Arabic Light Stemmer for IR in Medical Documents." Computers, Materials & Continua 68, no. 1 (2021): 1255–69. http://dx.doi.org/10.32604/cmc.2021.016155.

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Atwan, Jaffar, Mohammad Wedyan, Qusay Bsoul, Ahmad Hammadeen, and Ryan Alturki. "The Use of Stemming in the Arabic Text and Its Impact on the Accuracy of Classification." Scientific Programming 2021 (November 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/1367210.

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The ongoing growth in the vast amount of digital documents and other data in the Arabic language available online has increased the need for classification methods that can deal with the complex nature of such data. The classification of Arabic plays a large and important role in many modern applications and interferes with other sciences, which start from search engines and do not end with the Internet of Things. However, addressing the Arab classification errors with high performance is largely insufficient to deal with the huge quantities to reveal the classification of Arab documents; whil
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Alomari, Ebtesam, Iyad Katib, Aiiad Albeshri, Tan Yigitcanlar, and Rashid Mehmood. "Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning." Sensors 21, no. 9 (2021): 2993. http://dx.doi.org/10.3390/s21092993.

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Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper
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Al-Smadi, Omar Ali, Radzuwan Ab Rashid, Hadeel Saed, and Yousef Houssni Zrekat. "A linguistic ethnography of negotiation of meaning in English for medical purposes classrooms." International Journal of English Language and Literature Studies 12, no. 4 (2023): 330–41. http://dx.doi.org/10.55493/5019.v12i4.4901.

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Negotiation of meaning refers to intricate processes through which interlocutors can modify their speech to ensure the production and reception of comprehensible input. This empirical study aimed to thoroughly investigate the multifaceted aspects of negotiation of meaning and identify the influential factors shaping this process during classroom interactions within an English for Medical Purposes (EMP) class at a medical college in Saudi Arabia. Employing an eclectic qualitative methodology that incorporates key principles of Linguistic Ethnography, this research presented a comprehensive anal
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Book chapters on the topic "Arabic Light stemmer"

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Abd, Dhafar Hamed, Wasiq Khan, Khudhair Abed Thamer, and Abir J. Hussain. "Arabic Light Stemmer Based on ISRI Stemmer." In Intelligent Computing Theories and Application. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84532-2_4.

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Conference papers on the topic "Arabic Light stemmer"

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Elrajubi, Osama Mohamed. "An improved Arabic light stemmer." In 2013 International Conference on Research and Innovation in Information Systems (ICRIIS). IEEE, 2013. http://dx.doi.org/10.1109/icriis.2013.6716682.

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Hussien, Sabria Mohammed, and Hazim J. Aburagheef. "Arabic light-based stemming: a comparative study among ligh10 stemmer, P-stemmer, and Conditional light stemmer." In 2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA). IEEE, 2021. http://dx.doi.org/10.1109/it-ela52201.2021.9773743.

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Kanan, Tarek, Odai Sadaqa, Ashraf Almhirat, and Emran Kanan. "Arabic Light Stemming: A Comparative Study between P-Stemmer, Khoja Stemmer, and Light10 Stemmer." In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2019. http://dx.doi.org/10.1109/snams.2019.8931842.

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Zeroual, Imad, Mohamed Boudchiche, Azzeddine Mazroui, and Abdelhak Lakhouaja. "Developing and performance evaluation of a new Arabic heavy/light stemmer." In BDCA'17: 2nd international Conference on Big Data, Cloud and Applications. ACM, 2017. http://dx.doi.org/10.1145/3090354.3090371.

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Kanaan, Ghassan, Riyad Al-Shalabi, Mohamad Ababneh, and Alaa Al-Nobani. "Building an effective rule-based light stemmer for Arabic language to inprove search effectiveness." In 2008 International Conference on Innovations in Information Technology (IIT). IEEE, 2008. http://dx.doi.org/10.1109/innovations.2008.4781687.

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Abainia, Kheireddine, and Hamza Rebbani. "Comparing the Effectiveness of the Improved ARLSTem Algorithm with Existing Arabic Light Stemmers." In 2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS). IEEE, 2019. http://dx.doi.org/10.1109/ictaacs48474.2019.8988118.

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