Classifying and diacritizing Arabic poems using deep recurrent neural networks

Abandah, Gheith A.; Khedher, Mohammed Z.; Abdel-Majeed, Mohammad; Mansour, Hamdi; Hulliel, Salma F; Bisharat, Lara M

doi:10.1016/j.jksuci.2020.12.002

Cited by 21 publications

(14 citation statements)

References 14 publications

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“…The scheme was composed of two parts: a data processing module and another model module. Similarly, Abandah et al [11] adopted a machine learning approach using 1,657 K verses of poems and prose to develop neural networks that automatically classify and discretize Arabic poetry. Also, Shedko [12] used an LSTM-based RNN, with the objective of generating a Byron-style poem, achieving, as a result, a guided word sequence generation based on the correspondence between the latent neural network representations and the semantic map.…”

Section: Related Workmentioning

confidence: 99%

Text prediction recurrent neural networks using long short-term memory-dropout

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Ruiz-Alvarado³

et al. 2023

IJEECS

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Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem "La Ciudad y los perros" which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.

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Section: Related Workmentioning

confidence: 99%

Text prediction recurrent neural networks using long short-term memory-dropout

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Ruiz-Alvarado³

et al. 2023

IJEECS

View full text Add to dashboard Cite

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“…Al-shaibani et al [ 30 ] by deep bidirectional recurrent neural networks classify the meter of Arabic poems without diacritizing. Abandah et al [ 31 ] use recurrent neural networks with bidirectional long short-term memory cells for diacritizing the input Arabic poems.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Yousefi [ 29 ] reports 92% of accuracy. Yousef et al [ 28 ] report overall accuracy of 96.38%, Al-shaibani et al [ 30 ] report more than 94% accuracy and Abandah et al [ 31 ] report an average accuracy of 97.27%.…”

Section: Background and Related Workmentioning

confidence: 99%

Automatic meter classification of Kurdish poems

Mahmudi

Veisi

2023

PLoS ONE

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Most of the classic texts in Kurdish literature are poems. Knowing the meter of the poems is helpful for correct reading, a better understanding of the meaning, and avoiding ambiguity. This paper presents a rule-based method for the automatic classification of the poem meter for the Central Kurdish language also known as Sorani. The metrical system of Kurdish poetry is divided into three classes quantitative, syllabic, and free verses. As the vowel length is not phonemic in the language, there are uncertainties in syllable weight and meter identification. The proposed method generates all the possible situations and then, by considering all lines of the input poem and the common meter patterns of Kurdish poetry, identifies the most probable meter type and pattern of the input poem. Evaluation of the method on a dataset from VejinBooks Kurdish corpus resulted in 97.3% of precision in meter type and 96.2% of precision in pattern identification.

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“…The proposed activation function outperforms than base functions in LSTM on multiple datasets in terms of accuracy. Abandah et al (2020) proposed a deep recurrent neural network to classify Arabic poems. The proposed model is able to classify the input text with an accuracy of 97.27% on a large dataset of 1,657,000 verses of poems.…”

Section: Related Workmentioning

confidence: 99%

Transformer network-based word embeddings approach for autonomous cyberbullying detection

Pericherla

Ilavarasan

2021

IJIUS

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PurposeNowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.Design/methodology/approachIn this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.FindingsThe proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.Originality/valueOne of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.

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Classifying and diacritizing Arabic poems using deep recurrent neural networks

Cited by 21 publications

References 14 publications

Text prediction recurrent neural networks using long short-term memory-dropout

Text prediction recurrent neural networks using long short-term memory-dropout

Automatic meter classification of Kurdish poems

Transformer network-based word embeddings approach for autonomous cyberbullying detection

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