Fully automated Arabic to English machine translation system: transfer-based approach of AE-TBMT

Shquier, Mohammed M. Abu; Alhawiti, Khaled M.

doi:10.1504/ijict.2017.084341

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…e feature vector set is constructed by three different feature extraction methods: first-level semantic code, three-level semantic code, and morphological information. (5) We construct a word sense disambiguation model based on semantic knowledge by using the feature vector set extracted from the training corpus. (6) We disambiguate the test corpus by constructing a good word sense disambiguation model.…”

Section: Establishment Of Word Sense Disambiguation Modelmentioning

confidence: 99%

“…Statistics on the authoritative Chinese disambiguation corpus show that these ambiguous words are used very frequently, about 42% [3]. e universality of ambiguous word distribution makes word sense disambiguation an important link in many applications related to natural language processing, such as machine translation, information extraction, and content analysis [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Wang

2020

Complexity

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In natural language, the phenomenon of polysemy is widespread, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key issue in the field of natural language processing. This paper introduces the more common statistical learning methods used in the field of word sense disambiguation. Using the naive Bayesian machine learning method and the feature vector set extracted and constructed by the Dice coefficient method, a semantic word disambiguation model based on semantics is realized. The results of comparative experiments show that the proposed method is better compared with known systems. This paper proposes a method for disambiguation of word segmentation in professional fields based on unsupervised learning. This method does not rely on professional domain knowledge and training corpus and only uses the frequency, mutual information, and boundary entropy information of the string in the test corpus to solve the problem of word segmentation ambiguity. The experimental results show that these three evaluation standards can solve the problem of word segmentation ambiguity in professional fields and improve the effect of word segmentation. Among them, the segmentation result using mutual information is the best, and the performance is stable.

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Section: Establishment Of Word Sense Disambiguation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Wang

2020

Complexity

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“…They show that their new program could provide an accuracy of 84%. Shquier & Alhawiti (2017) designed an Arabic-English Transfer-Based Machine Translation (AE-TBMT) system and tested its efficiency. They conclude that their system accurately handles Arabic texts with the highest percentage of 96.6%.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Google Image Translate in Rendering Arabic Signage into English

Almahasees

Mahmoud²

2022

WJEL

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When people travel to another country for work or leisure, they regularly need a medium to help them understand the written messages in other languages. Google Translate offers a new service: translating the content of images (texts) instantly and freely into 100 languages powered by the Neural Machine Translation approach (NMT). In this vein, the current research paper attempts to evaluate the accuracy of Google Image Translate service in rendering the texts printed on Arabic signage: banners and road and shop signs from Arabic into English. Besides, it aims to identify the capacity of Google Translate in rendering Arabic signage into English effectively without the help of human translators. The paper adopts the Linguistic Error Analysis Framework of Costa et al. (2015) in analyzing the output of Google image service in terms of orthography, grammar, lexis, and semantics. The paper shows that Google Translate made the following errors while rendering the content of images into English: mistranslation, omission, additions, wrong choice, misordering, subject-verb disagreement, and semantic errors. In conclusion, the Google Image Translate service helps the users configure the gist of the image. However, a human translator is still needed since MT may not provide an adequate and effective translation as humans do.

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“…These defects appear because of the special nature of the Arabic language with rich morphology. The Arabic language has complicated morphology, meaning each word may consist of one or more prefixes, a stem or root, and one or more suffixes [2].…”

Section: Introductionmentioning

confidence: 99%

Arabic open information extraction system using dependency parsing

El-Morsy

Hussein

Mousa

2022

IJECE

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<p>Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and thinking carefully about all possible text relations. Based on clause types' propositions as extractable relations and constituents' grammatical functions, the identities of corresponding clause types are established. The proposed system named Arabic open information extraction(AOIE) can extract highly scalable Arabic text relations while being domain independent. Implementing the proposed system handles the problem using supervised strategies while the system relies on unsupervised extraction strategies. Also, the system has been implemented in several domains to avoid information extraction in a specific field. The results prove that the system achieves high efficiency in extracting clauses from large amounts of text.</p>

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Fully automated Arabic to English machine translation system: transfer-based approach of AE-TBMT

Cited by 3 publications

References 16 publications

Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Evaluation of Google Image Translate in Rendering Arabic Signage into English

Arabic open information extraction system using dependency parsing

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