This jupyter notebook is a part of my assignment where I leverage the knowledge of Natural language processing, and perform entity recognition on scientific articles. This project uses Glove Embeddings for embedding the text corpus, and it also compares the accuracy of the entity recognition with domain specific embeddings. Different dropout parameters are tested, and corresponding F1 scores are evaluated and finally the LSTM model is trained with the best optimal dropout parameter.
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This jupyter notebook is a part of my assignment where I leverage the knowledge of Natural language processing, and perform entity recognition on scientific articles. This project uses Glove Embeddings for embedding the text corpus, and it also compares the accuracy of the entity recognition with domain specific embeddings. Different dropout par…
DeepakGuru1998/Biomedical-Entity-Recognition
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This jupyter notebook is a part of my assignment where I leverage the knowledge of Natural language processing, and perform entity recognition on scientific articles. This project uses Glove Embeddings for embedding the text corpus, and it also compares the accuracy of the entity recognition with domain specific embeddings. Different dropout par…
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