Arabic Light Stemming: A Comparative Study between P-Stemmer, Khoja Stemmer, and Light10 Stemmer

Kanan, Tarek; Sadaqa, Odai; Almhirat, Ashraf; Kanan, Emran

doi:10.1109/snams.2019.8931842

Cited by 20 publications

(11 citation statements)

References 24 publications

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“…Therefore, eliminating these phrases won't have an impact on the classifier's performance because they have no impact on the classification task, in addition to dimensionality reduction, which is important in most ML tasks Table 1. The stem word is a crucial component that modern indexing and search engines support [36]. Information retrieval systems, NLP systems, and text extraction applications include indexing and searching.…”

Section: Removing Stop Wordsmentioning

confidence: 99%

“…To find the word's root, it then compares the remaining words to nouns (nouns) and linguistic patterns. After eliminating the biggest prefix and suffix from the word, the algorithm compares the remaining portion of the word with its verbal and nominal patterns to determine the root [40][41][42]. Snowball Stemmer: This derivation algorithm, sometimes referred to as the porter2 derivation algorithm, is an improvement on the Porter Stemmer algorithm because several of its flaws have been Tashaphyne Stemmer: It is a light Arabic derivative and syllable [43].…”

Section: Removing Stop Wordsmentioning

confidence: 99%

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Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Hawraa Fadhil Khelil,

Mohammed Fadhil Ibrahim,

Hafsa Ataallah Hussein

et al. 2024

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Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.

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Section: Removing Stop Wordsmentioning

confidence: 99%

Section: Removing Stop Wordsmentioning

confidence: 99%

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Hawraa Fadhil Khelil,

Mohammed Fadhil Ibrahim,

Hafsa Ataallah Hussein

et al. 2024

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show abstract

“…Some recent researches explored the effects of different stemming approaches on TC and text mining tasks. In [48] compared three different stemmers [11], [16], [49] on TC task. For classification the authors used SVM and NB.…”

Section: Related Workmentioning

confidence: 99%

Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

Almuzaini

Azmi

2020

IEEE Access

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Document classification is a classical problem in information retrieval, and plays an important role in a variety of applications. Automatic document classification can be defined as content-based assignment of one or more predefined categories to documents. Many algorithms have been proposed and implemented to solve this problem in general, however, classifying Arabic documents is lagging behind similar works in other languages. In this paper, we present seven deep learning-based algorithms to classify the Arabic documents. These are: Convolutional Neural Network (CNN), CNN-LSTM (LSTM = Long Short-Term Memory), CNN-GRU (GRU = Gated Recurrent Units), BiLSTM (Bidirectional LSTM), BiGRU, Att-LSTM (Attention-based LSTM), and Att-GRU. And for word representation, we applied the word embedding technique (Word2Vec). We tested our approach on two large datasets-with six and eight categories-using ten-fold cross-validation. Our objective was to study how the classification is affected by the stemming strategies and word embedding. First, we looked into the effects of different stemming algorithms on the document classification with different deep learning models. We experimented with eleven different stemming algorithms, broadly falling into: root-based and stem-based, and no stemming. We performed ANOVA test on the classification results using the different stemmers, which helps assure if the results are significant. The results of our study indicate that stem-based algorithms perform slightly better compared to root-based algorithms. Among the deep learning models, the Attention mechanism and the Bidirectional learning gave outstanding performance with Arabic text categorization. Our best performance is F -score = 97.96%, achieved using the Att-GRU model with stem-based algorithm. Next, we looked into different controlling parameters for word embedding. For Word2Vec, both skip-gram and bag-of-words (CBOW) perform well with either stemming strategies. However, when using a stem-based algorithm, skipgram achieves good results with a vector of smaller dimension, while CBOW requires a larger dimension vector to achieve a similar performance.

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“…Artificial Intelligence (AI) is a field of computer science that aims to create machines and software that can perform tasks that would typically require human intelligence, such as perception, reasoning, learning, and decision-making [33,18,17]. AI technologies include machine learning, natural language processing, computer vision, and robotics.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models

AlZu'bi,

Mughaid,

Quiam

et al. 2023

AIA

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ChatGPT, an AI-powered chatbot developed by OpenAI, has gained immense popularity since its public launch in November 2022. With its ability to write essays, emails, poems, and even computer code, it has become a useful tool for professionals in various fields. However, ChatGPT's responses are not always rooted in reality and are instead generated by a GAN. This paper aims to build a text classification model for a chatbot using Python. The model is trained on a dataset consisting of customer responses to a survey and their corresponding class labels. Many classifiers are trained and tested, such as Naive Bayes, Random Forest, Extra Trees, and Decision Trees. The results show that the Extra Trees classifier performs the best with an accuracy of 90%. The system demonstrates the importance of text preprocessing and selecting appropriate classifiers for text classification tasks in building an effective chatbot. In this paper, we also explore the capabilities and limitations of ChatGPT and its alternatives in 2023. We present a comprehensive overview of the alternative's performance. The work here, concludes with a discussion of the future directions of large language models and their impact on society and technology.

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Arabic Light Stemming: A Comparative Study between P-Stemmer, Khoja Stemmer, and Light10 Stemmer

Cited by 20 publications

References 24 publications

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models

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