A model for generating Arabic text from semantic representation

Ismail, Sally; Aref, Mostafa; Moawad, Ibrahim F.

doi:10.1109/icenco.2015.7416335

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…In 2015, Sally S.Ismail et al [27], the paper explained the Rich Semantic Graph to Text module's solution method. They illustrate the steps taken and the mechanism used to produce the module.…”

Section: Literature Reviewmentioning

confidence: 99%

State of Art for Semantic Analysis of Natural Language Processing

Maulud

Zeebaree

Jacksi

et al. 2021

QAJ

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Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

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Section: Literature Reviewmentioning

confidence: 99%

State of Art for Semantic Analysis of Natural Language Processing

Maulud

Zeebaree

Jacksi

et al. 2021

QAJ

View full text Add to dashboard Cite

show abstract

“…Of the ensemble approaches, Extremely Randomized Trees achieves the highest classification rate. Sally S.Ismail, et al [100]. Suggested solution system for the Rich Semantic Graph to the Text module, one of three modules in ongoing research on abstractive Arabic Text Summarization.…”

Section: Topic Modelingmentioning

confidence: 99%

Review on Natural Language Processing Based on Different Techniques

Maulud

Ameen

Omar

et al. 2021

AJRCoS

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With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error. The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.

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“…For Abstractive Arabic text summarization, S.Ismail et al [13] are working on single document summarization. Their proposed system consisted of three modules, first they convert the input Arabic text into a semantic graph called Rich Semantic Graph (RSG).…”

Section: Previous Workmentioning

confidence: 99%

A Proposed Textual Graph Based Model for Arabic Multi-document Summarization

Alwan¹,

Onsi²

2016

ijacsa

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Abstract-Text summarization task is still an active area of research in natural language preprocessing. Several methods that have been proposed in the literature to solve this task have presented mixed success. However, such methods developed in a multi-document Arabic text summarization are based on extractive summary and none of them is oriented to abstractive summary. This is due to the challenges of Arabic language and lack of resources. In this paper, we present a minimal languagedependent processing abstractive Arabic multi-document summarizer. The proposed model is based on textual graph to remove multi-document redundancy and generate coherent summary. Firstly, the original text, highly redundant and related multidocument, will be converted into textual graph. Next, graph traversal with structural rules will be applied to concatenate related sentences to single ones. Finally, unwanted and less weighted phrases will be removed from the summarized sentences to generate final summary. Preliminary results show that the proposed method has achieved promising results for multidocument summarization.

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A model for generating Arabic text from semantic representation

Cited by 5 publications

References 2 publications

State of Art for Semantic Analysis of Natural Language Processing

State of Art for Semantic Analysis of Natural Language Processing

Review on Natural Language Processing Based on Different Techniques

A Proposed Textual Graph Based Model for Arabic Multi-document Summarization

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