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Natural Language Processing Assignment Help

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Extracting Entities from Text in Natural Language Processing

Python Code import spacy # Load the English language model nlp = spacy.load("en_core_web_sm") # Sample text for Named Entity Recognition text = "Apple Inc. is headquartered in Cupertino, California. It was founded by Steve Jobs." # Process the text using spaCy doc = nlp(text) # Extract named entities entities = [(entity.text, entity.label_) for entity in doc.ents] # Print named entities and their labels for entity, label in entities: print(f"Entity: {entity}, Label: {label}")

This code demonstrates how spaCy can be used for Named Entity Recognition, identifying and categorizing named entities like organizations (e.g., Apple Inc.), locations (e.g., Cupertino, California), and people (e.g., Steve Jobs) in a given text.

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• Named Entity Recognition (NER): Our experts help to solve assignments with Identifying and classifying named entities in text, such as people, organizations, locations, dates, and more, which is useful for information extraction tasks.
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