A Semi-Supervised Method for Arabic Word Sense Disambiguation Using a Weighted Directed Graph

Abstract

In this paper, we propose a new semisupervised approach for Arabic word sense disambiguation. Using the corpus and Arabic Wordnet 1 , we define a method to cluster the sentences containing ambiguous words. For each sense, we generate a cluster that we use to construct a semantic tree. Furthermore, we construct a weighted directed graph by matching the tree of the original sentence with semantic trees of each sense candidate. To find the correct sense, we use a similarity score based on three collocation measures that will be classified using a novel voting procedure. The proposed method gives a high rate of recall and precision.