Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6908
Title: ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD
Authors: Al-Hajj, Moustafa 
Jarrar, Mustafa 
Issue Date: Sep-2021
Publisher: INCOMA Ltd.
Source: Al-Hajj, M., Jarrar, M., (2021). ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD. In Proceedings – the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), PP 40--48.
Series/Report no.: Proceedings – the International Conference on Recent Advances in Natural Language Processing (RANLP 2021);
Abstract: Using pre-trained transformer models such as BERT has proven to be effective in many NLP tasks. This paper presents our work to finetune BERT models for Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a sentence-pair binary classification task. First, we constructed a dataset of labeled Arabic context-gloss pairs (∼167k pairs) we extracted from the Arabic Ontology and the large lexicographic database available at Birzeit University. Each pair was labeled as True or False and target words in each context were identified and annotated. Second, we used this dataset for fine-tuning three pretrained Arabic BERT models. Third, we experimented the use of different supervised signals used to emphasize target words in context. Our experiments achieved promising results (accuracy of 84%) although we used a large set of senses in the experiment.
URI: http://hdl.handle.net/20.500.11889/6908
DOI: 10.26615/978-954-452-072-4_005
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