# Advancing the Arabic WordNet: Elevating Content Quality

arXiv: https://arxiv.org/abs/2403.20215 · HTML: https://mlsanity.com/paper/2403.20215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content](https://mlsanity.com/paper/2205.09664/) (2022)
- [Modeling Color Terminology Across Thousands of Languages](https://mlsanity.com/paper/1910.01531/) (2019)
- [Lexical Diversity in Kinship Across Languages and Dialects](https://mlsanity.com/paper/2308.13056/) (2023)

## Built on by (most cited first, 1 of 1)

- [Crowdsourcing Lexical Diversity](https://mlsanity.com/paper/2410.23133/) (2024)

## Abstract (arXiv)

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.