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Deep Learning for Named Entity Recognition by Zerihun Yitayew is a document available to read on EtoBox.

This document describes using a deep learning approach with ELMo embeddings and a bi-directional LSTM for named entity recognition (NER). NER aims to locate and classify named entities in text into predefined categories like person names, organizations, locations etc. The approach uses pre-trained ELMo embeddings to capture word context and meaning, and a bi-LSTM to understand sequences of words and labels. The model achieves good performance, with an F1-score of 81.2% for NER and 97.1% for part-of-speech t

Author

Zerihun Yitayew

Language

EN