Word agreement and ordering in English-Arabic machine translation

Shquier, Mohammed M. Abu; Sembok, Tengku Mohd Tengku

doi:10.1109/itsim.2008.4631625

Cited by 7 publications

(11 citation statements)

References 3 publications

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“…First technique uses a database to store all full-form words. Using full-form words means that the root and irregular forms are stored in a database (Abu Shquier and Sembok, 2008). Each of the items ‫و‬ ‫و‬ ( ) will all be entered explicitly into the database to be identified as relating to the same root ( ).…”

Section: Methodsmentioning

confidence: 99%

“…Second technique uses a morphological analysis component to analyze words and identify them as roots and affixes or prefix. A morphological analysis component is a rule-based module which is able to analyze a word and relate it to its root form (Abu Shquier and Sembok, 2008).Its advantages and disadvantages are the opposite of the full-form technique. It can capture morphological generality and identify newly-formed words.…”

Section: Methodsmentioning

confidence: 99%

“…These approaches are: rulebased, knowledge-based, corpus-based and hybrid MT. Rule-based machine translation approaches can be classified into the following categories: direct machine translation, interlingua machine translation and transfer-based machine translation (Abu Shquier and Sembok, 2008). This study adopts the transfer-based machine translation.…”

Section: Introductionmentioning

confidence: 99%

“…The system is implemented in Prolog and the parser is written in DCG formalism. Abu Shquier and Sembok (2008) presented the word agreement and ordering in English-Arabic machine translation by using rule-based approach. On the contrary, little work has been done in developing Arabic-English MT systems.…”

Section: Introductionmentioning

confidence: 99%

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Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach

Shirko

Omar

Arshad

et al. 2010

Journal of Computer Science

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Problem statement: Any Arabic to English Machine Translation (MT) system should be capable of dealing with word order which Arabic exhibits. This poses a significant challenge to MT due to the vast number of ways to express the same sentence in Arabic. The ordering features are very important and should be carefully applied to ensure the generation of sentence in the target language. Because they apply to the target language, it should fulfill the specific requirement of this language. Mistakes in the MT output can be either the result of analysis problems at the source language level, or due to generation problem at target language level. Word order rules are crucial for the generation of sentences in the target language. They also serve as rules for the ordering of sentence constituents. These rules draw their information from the syntactic knowledge. The word order problem becomes more obvious when making machine translation between languages that have rich morphological variations. Approach: The main objective of this research is to develop a machine translation that translates Arabic noun phrases into English by using transfer-based approach. A system called Npae-Rbmt has been developed in this research. Transfer-based machine translation is one instance of rule-based machine-translation approaches and is currently one of the most widely used methods of machine translation. The idea of transfer-based machine translation it is necessary to have an intermediate representation that captures the meaning of the original sentence in order to generate the correct translation. Using advantages of transfer-based machine translation such as analysis step, the Transfer-based becomes simpler as linguistic analysis goes deeper-as the representation of analysis step becomes more abstract. In fact, a major goal of MT research is to define a level of analysis which is so deep in which transfer-based machine translation is able to do. Results: The method was tested on 88 thesis titles and journals from the computer science domain. The accuracy of the result was 94.6%. These results proved the viability of this approach for distant languages. Conclusion: Based on the achieved results, we have managed to perform the syntactic reordering within an Arabic noun phrases to English translation task by using transfer-based machine translation and also achieved reasonable improvements in translation quality over related approach

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach

Shirko

Omar

Arshad

et al. 2010

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Fully automated, high quality machine translation (FAHQMT) has not yet been achieved. Yet there is a lot that we can do to improve the quality of MT output and increase its usefulness [1] …”

Section: Linguistic Knowledgementioning

confidence: 99%

Morphological Analysis and Generation for Machine Translation from and to Arabic

Bessou¹,

Touahria²

2011

IJCA

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Novel Prototype for Handling Arabic Natural Language Processing: Smart Morphological Analyser

Shquier

2019

2019 Second International Conference on Artificial Intelligence for Industries (AI4I)

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Word agreement and ordering in English-Arabic machine translation

Cited by 7 publications

References 3 publications

Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach

Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach

Morphological Analysis and Generation for Machine Translation from and to Arabic

Novel Prototype for Handling Arabic Natural Language Processing: Smart Morphological Analyser

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