Introduction

The KSAA-CAD shared task highlight the Contemporary Arabic Language Dictionary within the scenario of developing a Reverse Dictionary (RD) system and enhancing Word Sense Disambiguation (WSD) capabilities. The first KSAA-RD (Al-Matham et al., 2023) highlighted significant gaps in the domain of reverse dictionaries, which are designed to retrieve words by their meanings or definitions. This shared task comprises two tasks: RD and WSD. The RD task focuses on identifying word embeddings that most accurately match a given definition, termed a "gloss," in Arabic. Conversely, the WSD task involves determining the specific meaning of a word in context, particularly when the word has multiple meanings. KSAA-CAD presents novel directions for researchers to investigate and offer significant contributions to the discipline.

Registration 

Participants need to register via this link (https://forms.gle/SrApXnMrU7AHtRux6 )!

Tasks

1.1 Task 1: Reverse Dictionary

RDs, identified by their sequence-to-vector format, characterized as sequence-to-vector, introduce a differentiated strategy in contrast to traditional dictionary lookup methods. The RD task concentrates on the conversion of human-readable glosses into word embedding vectors.

This process entails reconstructing the word embedding vector corresponding to the defined word, a methodology aligning with the approaches of (Mickus et al., 2022; Zanzotto et al., 2010; Hill et al., 2016).

The dataset includes, lemma, lemma vector representations, and their respective gloss.

The developed model is expected to generate novel lemma vector representations for the unseen human-readable definitions in the test set. Such a strategy allows users to search for words based on anticipated definitions or meanings.

1.2 Task 2: Word Sense Disambiguation

WSD focuses on identifying the specific sense of a word in a given context. The WSD gloss-based approach is categorized as a knowledge-based WSD method. This approach utilizes external resources, especially dictionaries. This technique involves determining a word's intended meaning by calculating the overlap between its contextual use and the provided gloss or definition.

In the realm of contemporary Arabic language, dictionaries have been utilized in the development of gloss-based WSD datasets, as evidenced in the works of (Jarrar et al., 2023; El-Razzaz et al., 2021). These studies employed the Ahmed Mokhtar Omar dictionary (Omar, 2008). Furthermore, the research conducted by (Jarrar et al., 2023) also incorporated the Al-Ghani Al-Zaher dictionary (Abul-Azm, 2014).

The dataset consists of form, context, and context ID, and corresponding sense ID. The developed model is expected to retrieve the suitable sense ID for the form in the context from the WSD dictionary.