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- W4386996835 abstract "Arabic named entity recognition (NER) is a challenging problem, especially in conversational data such as social media posts. To address this problem, we propose an Arabic weak learner NER model called ANER-HMM, which leverages low quality predictions that provide partial recognition of entities. By combining these predictions, we achieve state of the art NER accuracy for cases for out-of-domain predictions. ANER-HMM leverages a hidden markov model to combine multiple predictions from weak learners and gazetteers. We demonstrate that ANER-HMM outperforms the state-of-the-art Arabic NER methods without requiring any labeled data or training deep learning models which often require large computing resources." @default.
- W4386996835 created "2023-09-25" @default.
- W4386996835 creator A5027134824 @default.
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- W4386996835 date "2023-09-16" @default.
- W4386996835 modified "2023-09-30" @default.
- W4386996835 title "Weak Supervision Approach for Arabic Named Entity Recognition" @default.
- W4386996835 doi "https://doi.org/10.5121/csit.2023.131602" @default.
- W4386996835 hasPublicationYear "2023" @default.
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