Abdulrahman Alosaimy

Also published as: Abdulrahman AlOsaimy


2023

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KSAA-RD Shared Task: Arabic Reverse Dictionary
Rawan Al-Matham | Waad Alshammari | Abdulrahman AlOsaimy | Sarah Alhumoud | Asma Wazrah | Afrah Altamimi | Halah Alharbi | Abdullah Alaifi
Proceedings of ArabicNLP 2023

This paper outlines the KSAA-RD shared task, which aims to develop a Reverse Dictionary (RD) system for the Arabic language. RDs allow users to find words based on their meanings or definition. This shared task, KSAA-RD, includes two subtasks: Arabic RD and cross-lingual reverse dictionaries (CLRD). Given a definition (referred to as a “gloss”) in either Arabic or English, the teams compete to find the most similar word embeddings of their corresponding word. The winning team achieved 24.20 and 12.70 for RD and CLRD, respectively in terms of rank metric. In this paper, we describe the methods employed by the participating teams and offer an outlook for KSAA-RD.

2018

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Web-based Annotation Tool for Inflectional Language Resources
Abdulrahman Alosaimy | Eric Atwell
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts
Areej Alshutayri | Eric Atwell | Abdulrahman Alosaimy | James Dickins | Michael Ingleby | Janet Watson
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

This paper describes an Arabic dialect identification system which we developed for the Discriminating Similar Languages (DSL) 2016 shared task. We classified Arabic dialects by using Waikato Environment for Knowledge Analysis (WEKA) data analytic tool which contains many alternative filters and classifiers for machine learning. We experimented with several classifiers and the best accuracy was achieved using the Sequential Minimal Optimization (SMO) algorithm for training and testing process set to three different feature-sets for each testing process. Our approach achieved an accuracy equal to 42.85% which is considerably worse in comparison to the evaluation scores on the training set of 80-90% and with training set “60:40” percentage split which achieved accuracy around 50%. We observed that Buckwalter transcripts from the Saarland Automatic Speech Recognition (ASR) system are given without short vowels, though the Buckwalter system has notation for these. We elaborate such observations, describe our methods and analyse the training dataset.