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A comprehensive dataset for Arabic word sense disambiguation
1Computing and Applied Technology, College of Technological Innovation, Zayed University, UAE.
Data in Brief
|July 5, 2024
Summary
This study introduces a new Arabic dataset for word sense disambiguation, covering 100 polysemous words and 367 senses. It aids Arabic Natural Language Processing (NLP) by including rare senses and diverse contexts.
Area of Science:
- Computational Linguistics
- Natural Language Processing
- Artificial Intelligence
Background:
- Arabic language exhibits significant polysemy, posing challenges for NLP tasks.
- Existing datasets may lack comprehensive coverage of diverse word senses, especially rare ones.
- Accurate word sense disambiguation is crucial for advancing Arabic NLP applications.
Purpose of the Study:
- To introduce a novel, comprehensive dataset for Arabic word sense disambiguation.
- To provide a resource that mitigates bias by including all senses, including rare ones.
- To support the development of more robust Arabic NLP models.
Main Methods:
- Collected data from diverse web sources (news, medicine, finance).
- Included 10 contextual sentences per word sense, totaling 3670 samples for 100 polysemous words.
- Utilized GPT3.5-turbo for generating synthetic data for underrepresented rare senses.
Main Results:
- Developed a dataset with 367 unique senses for 100 Arabic polysemous words.
- Dataset spans multiple domains and includes both real-world and synthetically generated data.
- Ensures coverage of all word senses, including infrequently used ones, to reduce model bias.
Conclusions:
- The dataset is a valuable resource for Arabic word sense disambiguation and NLP research.
- Its comprehensive nature and inclusion of rare senses enhance model training and reduce bias.
- The dataset has potential applications beyond disambiguation, including sentiment analysis.
Keywords:
Arabic languageDeep learningGPT3.5Labelled dataMachine learningNatural language processingWord sense disambiguationMore Related Videos
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