Machine-Translation History and Evolution: Survey for Arabic-English Translations

Alsohybe, Nabeel T.; Dahan, Neama Abdulaziz; Ba-Alwi, Fadl Mutaher

doi:10.9734/cjast/2017/36124

Cited by 15 publications

(11 citation statements)

References 47 publications

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“…Its techniques include word based, phrase based, syntax-based and hierarchal phrase based translations [16]. It is good as a dictionary for words, idioms or famous clauses [2]. However, its translation is not always correct, because the final translation depends on a high rate that is generated by the clustering algorithms according to the more accepted translations [10].…”

Section: Related Workmentioning

confidence: 99%

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Extending A Model for Ontology-Based Arabic-English Machine Translation (NAN)

Dahan¹,

Ba-Alwi²

2019

IJAIA

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The acceleration in telecommunication needs leads to many groups of research, especially in communication facilitating and Machine Translation fields. While people contact with others having different languages and cultures, they need to have instant translations. However, the available instant translators are still providing somewhat bad Arabic-English Translations, for instance when translating books or articles, the meaning is not totally accurate. Therefore, using the semantic web techniques to deal with the homographs and homonyms semantically, the aim of this research is to extend a model for the ontology-based Arabic-English Machine Translation, named NAN, which simulate the human way in translation. The experimental results show that NAN translation is approximately more similar to the Human Translation than the other instant translators. The resulted translation will help getting the translated texts in the target language somewhat correctly and semantically more similar to human translations for the Non-Arabic Natives and the Non-English natives.

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Section: Related Workmentioning

confidence: 99%

“…The ontology files are used in this type of MTs as a knowledge-base to store and retrieve the requested translations. The single SWMT may have many ontology files; each one for a specific context from a general preidentified context [2]. However, just one ontology file can be used at a time.…”

Section: Related Workmentioning

confidence: 99%

Extending A Model for Ontology-Based Arabic-English Machine Translation (NAN)

Dahan¹,

Ba-Alwi²

2019

IJAIA

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“…Artificial intelligence (AI) is one of the main areas of research and development in the world today. Many types of AI systems have been developed recently and have provided impressive solution in applications such as voice-powered virtual assistants (e.g., Siri and Alexa) [1], autonomous vehicles (e.g., Tesla) [2], robotics (e.g., in manufacturing cars) [3], and automatic translation (e.g., Google translate) [4]. Many AI-based solutions are also developed in the area of assistive technology, in particular for assisting the visually impaired (VI) persons.…”

Section: Introductionmentioning

confidence: 99%

Assisting the Visually Impaired in Multi-object Scene Description Using OWA-Based Fusion of CNN Models

2020

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Advances in technology can provide a lot of support for visually impaired (VI) persons. In particular, computer vision and machine learning can provide solutions for object detection and recognition. In this work, we propose a multi-label image classification solution for assisting a VI person in recognizing the presence of multiple objects in a scene. The solution is based on the fusion of two deep CNN models using the induced ordered weighted averaging (OWA) approach. Namely, in this work, we fuse the outputs of two pre-trained CNN models, VGG16 and SqueezeNet. To use the induced OWA approach, we need to estimate a confidence measure in the outputs of the two CNN base models. To this end, we propose the residual error between the predicted output and the true output as a measure of confidence. We estimate this residual error using another dedicated CNN model that is trained on the residual errors computed from the main CNN models. Then, the OAW technique uses these estimated residual errors as confidence measures and fuses the decisions of the two main CNN models. When tested on four image datasets of indoor environments from two separate locations, the proposed novel method improves the detection accuracy compared to both base CNN models. The results are also significantly better than state-of-the-art methods reported in the literature.

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“…Deep learning is more expressive than traditional methods in data analysis. It has been used widely in engineering applications and gained great achievements in recent years, such as deep generative models, machine translations, attention in deep models, one-shot learning, style transfers, deep unsupervised learning in the past few years [33][34][35][36][37][38]. In our case, each dataset is a 1×101-dimensional series describing distribution of photon energy in transverse space, where the energy on each space point is a feature.…”

Section: Introductionmentioning

confidence: 99%

Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks

Wang

Yang

et al. 2019

New J. Phys.

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During oscillations of cosmology inflation around the minimum of a cuspy potential after inflation, the existence of extra high frequency gravitational waves (HFGWs) (∼GHz) has been proven effectively recently. Based on the electromagnetic resonance system for detecting such extra HFGWs, we adopt a new data processing scheme to identify the corresponding GW signal, which is the transverse perturbative photon fluxes (PPF). In order to overcome the problems of low efficiency and high interference in traditional data processing methods, we adopt deep learning to extract PPF and make some source parameters estimation. Deep learning is able to provide an effective method to realize classification and prediction tasks. Meanwhile, we also adopt anti-overfitting technique and make adjustment of some hyperparameters in the course of study, which improve the performance of classifier and predictor to a certain extent. Here the convolutional neural network (CNN) is used to implement deep learning process concretely. In this case, we investigate the classification accuracy varying with the ratio between the number of positive and negative samples. When such ratio exceeds to 0.11, the accuracy could reach up to 100%. Besides, we also investigate the classification accuracy with different amplitude of extra HFGWs. As a predictor, the mean relative error of parameters estimation decreases when the amplitude of extra HFGWs increases. Especially, when amplitude h(t) is in 10 −31 -10 −30 the mean relative error reaches around 0.014. On the contrary, the mean relative error increases with frequency increasing in 10 8 -10 11 Hz. At the optimal resonance frequency 5×10 9 Hz, the mean relative error is 0.12. Then we also study the mean relative error varying with waist radius W 0 of Gaussian beam, its optimal value is 0.138 when W 0 is in (0.05 m, 0.1 m) approximately. Compared with classifiers and predictors using other machine learning algorithms, deep CNN for our datasets has higher accuracy and lower error.

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Machine-Translation History and Evolution: Survey for Arabic-English Translations

Cited by 15 publications

References 47 publications

Extending A Model for Ontology-Based Arabic-English Machine Translation (NAN)

Extending A Model for Ontology-Based Arabic-English Machine Translation (NAN)

Assisting the Visually Impaired in Multi-object Scene Description Using OWA-Based Fusion of CNN Models

Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks

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