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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
