How to Use SpaCy for POS Tagging and Named Entity Recognition in Python

Text tagging is a foundational step in natural language processing that helps identify the role and type of each word in a body of text. Part-of-Speech (POS) tagging classifies words as nouns, verbs, adjectives, and more by iterating through tokens in a SpaCy document. Named Entity Recognition (NER) goes further by identifying real-world entities such as people, places, and organizations using the document's `.ents` property. SpaCy's English model `en_core_web_sm` powers both techniques, relying on machine learning and a transition-based algorithm under the hood. Notably, stripping punctuation or capitalization from input text can reduce NER accuracy, so preserving natural text formatting is recommended for best results.