Word sense disambiguation for Arabic text using Wikipedia and Vector Space Model

In this research we introduce a new approach for Arabic word sense disambiguation by utilizing Wikipedia as a lexical resource for disambiguation. The nearest sense for an ambiguous word is selected using Vector Space Model as a representation and cosine similarity between the word context and the retrieved senses from Wikipedia as a measure. Three experiments have been conducted to evaluate the proposed approach, two experiments use the first retrieved sentence for each sense from Wikipedia but they use different Vector Space Model representations while the third experiment uses the first paragraph for the retrieved sense from Wikipedia. The experiments show that using first paragraph is better than the first sentence and the use of TF-IDF is better than using abstract frequency in VSM. Also, the proposed approach is tested on English words and it gives better results using the first sentence retrieved from Wikipedia for each sense.