Advancing the Arabic WordNet: Elevating Content Quality

TL;DR

This paper presents a significant revision of the Arabic WordNet, improving its correctness, completeness, and structural diversity to enhance its utility for NLP applications involving the Arabic language.

Contribution

The paper introduces a comprehensive update to the Arabic WordNet, correcting errors, adding missing information, and extending its structure with new elements like phrasets and lexical gaps.

Findings

Updated over 58% of synsets with corrections and additions

Enhanced the structure to better represent language diversity

Improved resource quality for NLP applications

Abstract

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.