P-Stemmer or NLTK Stemmer for Arabic Text Classification?

Elbes, Mohammed; Aldajah, Amal; Sadaqa, Odai

doi:10.1109/snams.2019.8931818

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Because of the multidisciplinary nature of qua-disciplinary terms, which have different and diametrically opposed meanings when preceded and followed by different collocations, Takhom et al [15] detected ambiguous interdisciplinary terms by using web-based text analysis. The NLTK (Natural Language Toolkit) technique incorporates lexical annotation features, Elbes et al [16] collected news articles using SVM (Support Vector Machine) and NLTK, and classified the data set. In addition, the lexical screening function is used in essay writing activities, Contreras et al [17] used NLTK to apply natural language processing algorithms to improve essay ratings.…”

Section: Lexical Constructionmentioning

confidence: 99%

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

Hu¹,

Ren²,

Jiang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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An obviously challenging problem in named entity recognition is the construction of the kind data set of entities. Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news. This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology. Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts, this paper designs a literature content extraction method that allows dealing with complex text structures. Secondly, based on the literature extraction content, we formalize the entity extraction task (lexicon and lexical-based entity extraction) for entity extraction. Furthermore, for testing the accuracy of entity extraction, three currently popular recognition methods are chosen to perform entity detection in this paper. Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task. This study presents a pilot study of entity extraction, which involves the use of a complex structure and specialized literature on carbonate platforms in English.

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Section: Lexical Constructionmentioning

confidence: 99%

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

Hu¹,

Ren²,

Jiang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…They reported their highest accuracy score of 92% using the CNN model. While [52] compared two stemmers [49], [53] to see which one is better suited for Arabic TC. Using two different classifiers, SVM and NB, and a dataset of 1000 news articles from alghad.com, they reported their best performance of F -score = 90% when used with the stemmer [49].…”

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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Comparative Multinomial Text Classification Analysis of Naïve Bayes and XGBoost with SMOTE on Imbalanced Dataset

Chaturvedi

Yadav

Ansari³

et al. 2021

Computational Intelligence in Pattern Recognition

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P-Stemmer or NLTK Stemmer for Arabic Text Classification?

Cited by 7 publications

References 25 publications

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

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

Comparative Multinomial Text Classification Analysis of Naïve Bayes and XGBoost with SMOTE on Imbalanced Dataset

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