A Supervised Approach for Word Sense Disambiguation based on Arabic Diacritics

Since the last two decades' Arabic natural language processing (ANLP) has become increasingly much more important. One of the key issues related to ANLP is ambiguity. In Arabic language different pronunciation of one word may have a different meaning. Furthermore, ambiguity also has an impact on the effectiveness and efficiency of Machine Translation (MT). The issue of ambiguity has limited the usefulness and accuracy of the translation from Arabic to English. The lack of Arabic resources makes ambiguity problem more complicated. Additionally, the orthographic level of representation cannot specify the exact meaning of the word. This paper looked at the diacritics of Arabic language and used them to disambiguate an ambiguous word. The proposed approach of word sense disambiguation used Diacritizer application to Diacritize Arabic text. Then find the most accurate sense of an ambiguous word using Naive Bayes Classifier. Our system gets 91% precision, and 12.11% error rate. This experimental study proves that using Arabic Diacritics with Naive Bayes Classifier enhances the accuracy of choosing the appropriate sense for ambiguous Arabic words.