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Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
2021
Applied Sciences
Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. In this paper, we propose a deep context-aware bidirectional long short-term memory (CaBiLSTM) model for the Sindhi NER task. The model relies upon
doi:10.3390/app11199038
fatcat:xlkuygvsk5c2nc7knvdni3qcsq