A Scalable Shallow Learning Approach for Tagging Arabic News Articles

Qadi, Leen Al; Rifai, Hozayfa El; Obaid, Safa; Elnagar, Ashraf

doi:10.5455/jjcit.71-1585409230

Cited by 5 publications

(5 citation statements)

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“…2019; Al Qadi et al . 2020; Elnagar et al . 2020; Nassif, Darya, and Elnagar 2021a; El Rifai, Al Qadi, and Elnagar 2022).…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

“…For RNN, we experimented with both the Bidirectional Long Short-Term Memory (BiLSTM) layer and Bidirectional Gated Recurrent Unit (BiGRU). We selected these three models because of their good performance on similar tasks (Al Qadi et al 2019;Al Qadi et al 2020;Elnagar et al 2020;Nassif, Darya, and Elnagar 2021a;El Rifai, Al Qadi, and Elnagar 2022).…”

Section: Extrinsic Assessmentmentioning

confidence: 99%

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A benchmark for evaluating Arabic word embedding models

2022

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Modelling the distributional semantics of such a morphologically rich language as Arabic needs to take into account its introflexive, fusional, and inflectional nature attributes that make up its combinatorial sequences and substitutional paradigms. To evaluate such word distributional models, the benchmarks that have been used thus far in Arabic have mimicked those in English. This paper reports on a benchmark that we designed to reflect linguistic patterns in both Contemporary Arabic and Classical Arabic, the first being a cover term for written and spoken Modern Standard Arabic, while the second for pre-modern Arabic. The analogy items we included in this benchmark are chosen in a transparent manner such that they would capture the major features of nouns and verbs; derivational and inflectional morphology; high-, middle-, and low-frequency patterns and lexical items; and morphosemantic, morphosyntactic, and semantic dimensions of the language. All categories included in this benchmark are carefully selected to ensure proper representation of the language. The benchmark consists of 45 roots of the trilateral, all-consonantal, and semivowel-inclusive types; six morphosemantic patterns (’af‘ala; ifta‘ala; infa‘ala; istaf‘ala; tafa‘‘ala; and tafā‘ala); five derivations (the verbal noun, active participle, and the contrasts in Masculine-Feminine; Feminine-Singular-Plural; Masculine-Singular-Plural); and morphosyntactic transformations (perfect and imperfect verbs conjugated for all pronouns); and lexical semantics (synonyms, antonyms, and hyponyms of nouns, verbs, and adjectives), as well as capital cities and currencies. All categories include an equal proportion of high-, medium-, and low-frequency items. For the purpose of validating the proposed benchmark, we developed a set of embedding models from different textual sources. Then, we tested them intrinsically using the proposed benchmark and extrinsically using two natural language processing tasks: Arabic Named Entity Recognition and Text Classification. The evaluation leads to the conclusion that the proposed benchmark is truly reflective of this morphologically rich language and discriminatory of word embeddings.

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“…2019; Al Qadi et al . 2020; Elnagar et al . 2020; Nassif, Darya, and Elnagar 2021a; El Rifai, Al Qadi, and Elnagar 2022).…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

Section: Extrinsic Assessmentmentioning

confidence: 99%

A benchmark for evaluating Arabic word embedding models

2022

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“…We evaluate and compare the two approaches using the custom accuracy metric specific to the multi-labeling system, along with hamming-loss scores. This work is an extension of our work [ 4 , 5 ] on single-label classification.…”

Section: Introductionmentioning

confidence: 91%

Arabic text classification: the need for multi-labeling systems

Rifai

Qadi

Elnagar

2021

Neural Comput & Applic

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The process of tagging a given text or document with suitable labels is known as text categorization or classification. The aim of this work is to automatically tag a news article based on its vocabulary features. To accomplish this objective, 2 large datasets have been constructed from various Arabic news portals. The first dataset contains of 90k single-labeled articles from 4 domains (Business, Middle East, Technology and Sports). The second dataset has over 290 k multi-tagged articles. To examine the single-label dataset, we employed an array of ten shallow learning classifiers. Furthermore, we added an ensemble model that adopts the majority-voting technique of all studied classifiers. The performance of the classifiers on the first dataset ranged between 87.7% (AdaBoost) and 97.9% (SVM). Analyzing some of the misclassified articles confirmed the need for a multi-label opposed to single-label categorization for better classification results. For the second dataset, we tested both shallow learning and deep learning multi-labeling approaches. A custom accuracy metric, designed for the multi-labeling task, has been developed for performance evaluation along with hamming loss metric. Firstly, we used classifiers that were compatible with multi-labeling tasks such as Logistic Regression and XGBoost, by wrapping each in a OneVsRest classifier. XGBoost gave the higher accuracy, scoring 84.7%, while Logistic Regression scored 81.3%. Secondly, ten neural networks were constructed (CNN, CLSTM, LSTM, BILSTM, GRU, CGRU, BIGRU, HANGRU, CRF-BILSTM and HANLSTM). CGRU proved to be the best multi-labeling classifier scoring an accuracy of 94.85%, higher than the rest of the classifies.

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“…Researchers have ventured into the utilization of ensemble techniques for emotion detection, with a specific focus on employing random forest averaging, locally weighted naive bayes (LWNB), logistic classifiers, boosting-treebased models, gradient boosting and majority-voting classifier [2] [3] [4] [5] [6]. Furthermore, Capsule Networks have garnered attention in the domain of Speech Emotion Recognition (SER) [7], including Capsule Network hybrids [8] [9] [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition using Hybrid Architectures

Norval,

Wang

2024

IJC

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The detection of human emotions from speech signals remains a challenging frontier in audio processing and human-computer interaction domains. This study introduces a novel approach to Speech Emotion Recognition (SER) using a Dendritic Layer combined with a Capsule Network (DendCaps). A Convolutional Neural Network (NN) and a Long Short-Time Neural Network (CLSTM) hybrid model are used to create a baseline which is then compared to the DendCap model. Integrating dendritic layers and capsule networks for speech emotion detection can harness the unique advantages of both architectures, potentially leading to more sophisticated and accurate models. Dendritic layers, inspired by the nonlinear processing properties of dendritic trees in biological neurons, can handle the intricate patterns and variabilities inherent in speech signals, while capsule networks, with their dynamic routing mechanisms, are adept at preserving hierarchical spatial relationships within the data, enabling the model to capture more refined emotional subtleties in human speech. The main motivation for using DendCaps is to bridge the gap between the capabilities of biological neural systems and artificial neural networks. This combination aims to capitalize on the hierarchical nature of speech data, where intricate patterns and dependencies can be better captured. Finally, two ensemble methods namely stacking and boosting are used for evaluating the CLSTM and DendCaps networks and the experimental results show that stacking of the CLSTM and DendCaps networks gives the superior result with a 75% accuracy.

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A Scalable Shallow Learning Approach for Tagging Arabic News Articles

Cited by 5 publications

References 0 publications

A benchmark for evaluating Arabic word embedding models

A benchmark for evaluating Arabic word embedding models

Arabic text classification: the need for multi-labeling systems

Speech Emotion Recognition using Hybrid Architectures

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