AWSS: An Algorithm for Measuring Arabic Word Semantic Similarity
Faaza A. Almarsoomi, James D. O’Shea, Zuhair A. Bandar, Keeley A. Crockett · 2013
Semantic similarity is an essential component of numerous applications in fields such as natural language processing, artificial intelligence, linguistics, and psychology. Most of the reported work has been done in English. To the best of our knowledge, there is no word similarity measure developed specifically for Arabic. This paper presents a method to measure the semantic similarity between two Arabic words in the Arabic knowledge base. The semantic similarity is calculated through the combination of the common and different attributes between the Arabic words in the hierarchy semantic net. We use a previously developed Arabic word benchmark dataset to optimize and evaluate the Arabic measure. Experimental evaluation indicates that the Arabic measure is performing well. It has achieved a correlation value of 0.894 compared with the average value of human participants of 0.893 on evaluation dataset.