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This paper uses a semantic technique by adopting a Rhetorical Structure Theory (RST) for summarization purpose, to discover the most significant paragraphs based on functional and semantic criteria. However, the quality of RST summarization suffers when dealing with large documents. This paper proposes a new hybrid summarization model for Arabic text, which(More)
In this paper, a novel for Query Translation and Expansion for enabling English/Arabic CLIR for both normal and OCR-Degraded Arabic Text model has been proposed, implemented, and tested. First, an English/Arabic Word Collocations Dictionary has been established plus reproducing three English/Arabic Single Words Dictionaries. Second, a modern Arabic Corpus(More)
The Rhetorical Structure Theory (RST) is a descriptive theory of a major aspect of the structure of natural text. It is applied in English as well as other languages such as, French and Japanese but there are still no clear efforts to apply RST in Arabic. This paper provides a framework to apply RST in Arabic, in order to improve the ability of extracting(More)
There is a remarkable growth in the usage of social networks, such as Facebook and Twitter. Users from different cultures and backgrounds post large volumes of textual comments reflecting their opinion in different aspect of life and make them available to everyone. In particular we study the case of Twitter and focus on presidential elections in Egypt(More)
Named Entity Recognition (NER) is a task in Information Extraction (IE). The Named Entity Recognition has become very important for Natural Language Processing (NLP). In this paper, we designed a system which enhanced the named entities recognition for Arabic language where the system was developed for Arabic nouns and entities extractions. The nouns(More)