2012
DOI: 10.1155/2012/620461
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Contribution to Semantic Analysis of Arabic Language

Abstract: We propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We u… Show more

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Cited by 13 publications
(8 citation statements)
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“…However, such measures are rarely used to compare multi-word phrases [4]. Three broad categories of semantic similarity detection methods are used to determine the level of similarity between words: Dictionary/ontology-based methods consider knowledge bases to gather the semantic information that is compared when determining semantic similarity [14]. Meanwhile, corpus-based methods primarily use word frequencies to determine semantic similarity.…”
Section: Related Workmentioning
confidence: 99%
See 3 more Smart Citations
“…However, such measures are rarely used to compare multi-word phrases [4]. Three broad categories of semantic similarity detection methods are used to determine the level of similarity between words: Dictionary/ontology-based methods consider knowledge bases to gather the semantic information that is compared when determining semantic similarity [14]. Meanwhile, corpus-based methods primarily use word frequencies to determine semantic similarity.…”
Section: Related Workmentioning
confidence: 99%
“…This is done based on statistics taken from extensive corpora. Finally, hybrid methods consider more than one information source to determine semantic similarity [14].…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…Arabic is a Semitic language known by its morphology richness and different written and spoken forms such as modern standard Arabic (MSA) and its various dialects. Arabic morphology and structure complexity create many challenges such as the shortage of large datasets and limited tools to perform sentiment analysis [ 3 , 4 ]. Even deep neural networks (DNNs) [ 5 ] and convolutional neural networks (CNNs) [ 6 ] have shown promising and encouraging performance, little research on sentiment analysis using deep learning (DL) techniques has been done for Arabic language [ 7 – 9 ] while many researches have been done on other languages [ 10 12 ].…”
Section: Introductionmentioning
confidence: 99%