Named Entity Recognition
Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that aims to identify and classify named entities in unstructured text into predefined categories such as person names, organization names, locations, medical codes, etc. NER is a crucial step in many NLP applications such as information extraction, question answering, sentiment analysis, and machine translation. NER systems use various techniques such as rule-based, statistical, and deep learning approaches to identify named entities. Rule-based approaches use handcrafted rules to identify named entities based on patterns and heuristics. Statistical approaches use machine learning algorithms such as Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) to learn patterns from annotated data. Deep learning approaches use neural networks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to learn features from raw text data.
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