Year: 2024
Type: conference-paper
Abstract: Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic.This paper describes our participation in the Contemporary Arabic Dictionary Shared Task.We propose two models that achieved first place in both tasks.We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction.We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year's winner.We had an accuracy score of 0.268 for the Word Sense Disambiguation task.
Authors: Serry Sibaee, Abdullah Alharbi, Samar Ahmad, Omer Nacar, Anis Koubaa, Lahouari Ghouti
Institutions: Prince Sultan University, King Abdulaziz University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Prince Sultan University
OpenAlex ID: W4402671447