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.

高质量的词网（WordNets）对于依赖此类资源的 NLP 应用程序来说非常重要。为了解决诸如词汇错误、缺失的释义和例句，以及语言形态和语义的不完善等问题，我们对阿拉伯语 WordNet 进行了重大修订，更新了超过 58% 的同义词集。通过添加缺失的信息和纠正错误，同时还扩展了词网结构，引入了新的元素：短语和词汇空缺，以应对语言多样性和不可翻译性的问题。

推进阿拉伯语词网：提升内容质量

Advancing the Arabic WordNet: Elevating Content Quality

This paper describes a method to enrich lexical resources with content
relating to linguistic diversity, based on knowledge from the field of lexical
typology. We capture the phenomenon of diversity through the notions of lexical
gap and language-specific word and use a systematic method to infer gaps
semi-automatically on a large scale. As a first result obtained for the domain
of kinship terminology, known to be very diverse throughout the world, we
publish a lexico-semantic resource consisting of 198 domain concepts, 1,911
words, and 37,370 gaps covering 699 languages. We see potential in the use of
resources such as ours for the improvement of a variety of cross-lingual NLP
tasks, which we demonstrate through a downstream application for the evaluation
of machine translation systems.

该论文描述了一种基于词汇类型学知识来增强词汇资源与语言多样性相关内容的方法，通过半自动化的系统方法来推断词汇差距，从而建立一个包含 198 个领域概念、1911 个单词和 37370 个差距的词汇语义资源，该资源涵盖了 699 种语言，并说明了该资源对于改善跨语言 NLP 任务具有潜在价值。同时，为证明该资源的可行性，利用该词库开发了一个 MT 系统评估程序。