Advancing the Arabic WordNet: Elevating Content Quality
This work addresses the need for high-quality lexico-semantic resources for Arabic NLP applications, though it is incremental as it builds on an existing WordNet.
The authors tackled the problem of low-quality WordNets in NLP by revising the Arabic WordNet to improve correctness and completeness, resulting in updates to over 58% of synsets and introducing new elements like phrasets and lexical gaps.
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.