The ability to identify the intended meanings of words in context is a central research topic in natural language. Many solutions exist for Word Sense Disambiguation WSD in different languages, such as English or French, but research on Arabic WSD remains limited. The main bottleneck is the lack of resources. In this paper, we show that it is possible to build a WSD system for the Arabic language thanks to the Arabic WordNet and its connections to the English Princeton WordNet. Given that the Arabic WordNet does not contain definitions for synsets, we construct a dictionary that maps the Princeton WordNet definitions to the Arabic WordNet. We also create an Arabic evaluation corpus and gold standard. We then exploit this dictionary and evaluation corpus to run and evaluate an adapted ant colony algorithm on Arabic text that can use the Lesk similarity measure thanks to definition mapping. The algorithm shows a performance of approximately 80% compared to the random baseline of 78.9%.
['Abdelaali Bakhouche', 'Tlili Yamina', 'Didier Schwab', 'Andon Tchechmedjiev']
Ant colony algorithm for Arabic word sense disambiguation through English lexical information