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KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis

Domain:

natural language processing

Record type:

papermodel
Creator:
AwlVeiAbd
Host:avatar
This paper enhances the study of sentiment analysis for the Central Kurdish language by integrating the Bidirectional Encoder Representations from Transformers (BERT) into Natural Language Processing techniques. Kurdish is a low-resourced language, having a high level of linguistic diversity with minimal computational resources, making sentiment analysis somewhat challenging. Earlier, this was done using a traditional word embedding model, such as Word2Vec, but with the emergence of new language models, specifically BERT, there is hope for improvements. The better word embedding capabilities of BERT lend to this study, aiding in the capturing of the nuanced semantic pool and the contextual intricacies of the language under study, the Kurdish language, thus setting a new benchmark for sentiment analysis in low-resource languages.

Visit

arxiv.org

Tasks

sentiment analysistext classification

Tags

Computation and LanguageArtificial Intelligence

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