Abstract
Named Entity Recognition (NER) is a task that identifies named entities from data written in Natural Language. It accepts sentences or paragraphs as input and identifies the relevant nouns like names of people, places, organizations, etc. that appear within the sentences or paragraph or an article. This model also belongs from the field of Information Extraction of Natural Language Processing (NLP). Lots of research works have been carried out on Named Entities Recognition (NER), but most of the works have been done on resource-rich languages and domains. It is very challenging to work with informal tweets which make the process more complex due to unstructured and incomplete data. In this paper, we use deep learning-based approach using bi-directional LSTM (Bi-LSTM) for recognizing named entities from tweets mixed in Hindi and English languages.
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Majumder, A., Paul, A., Banerjee, A. (2022). Deep Learning-Based Approach Using Word and Character Embedding for Named Entity Recognition from Hindi-English Tweets. In: Mandal, J.K., Hinchey, M., Sen, S., Biswas, P. (eds) Applications of Networks, Sensors and Autonomous Systems Analytics. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-7305-4_23
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DOI: https://doi.org/10.1007/978-981-16-7305-4_23
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