@inproceedings{alshammari-etal-2024-ksaa,
title = "{KSAA}-{CAD} Shared Task: Contemporary {A}rabic Dictionary for Reverse Dictionary and Word Sense Disambiguation",
author = "Alshammari, Waad and
Almazrua, Amal and
Al Wazrah, Asma and
Almatham, Rawan and
Alhoshan, Muneera and
Alosaimy, Abdulrahman",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of the Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.74/",
doi = "10.18653/v1/2024.arabicnlp-1.74",
pages = "677--685",
abstract = "This paper outlines the KSAA-CAD shared task, highlighting 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 RDs, 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. The winning team achieved the highest-ranking score of 0.0644 in RD using Electra embeddings. In this paper, we describe the methods employed by the participating teams and provide insights into the future direction of KSAA-CAD."
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<abstract>This paper outlines the KSAA-CAD shared task, highlighting 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 RDs, 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. The winning team achieved the highest-ranking score of 0.0644 in RD using Electra embeddings. In this paper, we describe the methods employed by the participating teams and provide insights into the future direction of KSAA-CAD.</abstract>
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%0 Conference Proceedings
%T KSAA-CAD Shared Task: Contemporary Arabic Dictionary for Reverse Dictionary and Word Sense Disambiguation
%A Alshammari, Waad
%A Almazrua, Amal
%A Al Wazrah, Asma
%A Almatham, Rawan
%A Alhoshan, Muneera
%A Alosaimy, Abdulrahman
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of the Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F alshammari-etal-2024-ksaa
%X This paper outlines the KSAA-CAD shared task, highlighting 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 RDs, 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. The winning team achieved the highest-ranking score of 0.0644 in RD using Electra embeddings. In this paper, we describe the methods employed by the participating teams and provide insights into the future direction of KSAA-CAD.
%R 10.18653/v1/2024.arabicnlp-1.74
%U https://aclanthology.org/2024.arabicnlp-1.74/
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.74
%P 677-685
Markdown (Informal)
[KSAA-CAD Shared Task: Contemporary Arabic Dictionary for Reverse Dictionary and Word Sense Disambiguation](https://aclanthology.org/2024.arabicnlp-1.74/) (Alshammari et al., ArabicNLP 2024)
ACL