Advancing the Arabic WordNet: Elevating Content Quality
Abed Alhakim Freihat Hadi Khalilia Gábor Bella Fausto Giunchiglia
High-quality WordNets are crucial for achieving high-quality results in NLP
applications that rely on such resources. However, the wordnets of most
languages suffer from serious issues of correctness and completeness with
respect to the words and word meanings they define, such as incorrect lemmas,
missing glosses and example sentences, or an inadequate, Western-centric
representation of the morphology and the semantics of the language. Previous
efforts have largely focused on increasing lexical coverage while ignoring
other qualitative aspects. In this paper, we focus on the Arabic language and
introduce a major revision of the Arabic WordNet that addresses multiple
dimensions of lexico-semantic resource quality. As a result, we updated more
than 58% of the synsets of the existing Arabic WordNet by adding missing
information and correcting errors. In order to address issues of language
diversity and untranslatability, we also extended the wordnet structure by new
elements: phrasets and lexical gaps.