Generating Sense Inventories for

Ambiguous Arabic Words

Marwah Alian¹ and Arafat Awajan^1,2

¹King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Jordan

²Information Technology College, Computer Science Department, Mutah University, Jordan

Abstract: The process of selecting the appropriate meaning of an ambigous word according to its context is known as word sense disambiguation. In this research, we generate a number of Arabic sense inventories based on an unsupervised approach and different pre-trained embeddings, such as Aravec, Fasttext, and Arabic-News embeddings. The resulted inventories from the pre-trained embeddings are evaluated to investigate their efficiency in Arabic word sense disambiguation and sentence similarity. The sense inventories are generated using an unsupervised approach that is based on a graph-based word sense inductionalgorithm. Results show that the Aravec-Twitter inventory achieves the best accuracy of 0.47 for 50 neighbors and a close accuracy to the Fasttext inventory for 200 neighbors while it provides similar accuracy to the Arabic-News inventory for 100neighbors. The experiment of replacing ambiguous words with their sense vectors is tested for sentence similarity using all sense inventories and the results show that using Aravec-Twitter sense inventoryprovides a better correlation value.

Keywords: Word sense induction, word sense disambiguation, arabic text, sense inventory.

Received February 25, 2021; accepted March 7, 2021