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    Entity Extraction

    Entity extraction is the process of identifying specific information from text, such as names, companies, locations, products, dates, or prices.

    Common Types of Entities

    • People: Real names or titles (e.g., Ada Lovelace)
    • Organizations: Companies, groups, or agencies (e.g., Google, United Nations)
    • Locations: Cities, states, or countries (e.g., Houston, Japan)
    • Dates and Times: Specific or relative time words (e.g., October 5, yesterday)
    • Values: Money, numbers, and percentages (e.g., $50, 10 items)

    How the Process Works

    • Tokenization: Breaking the raw text down into individual words.
    • Scanning: Looking at context and patterns to spot proper nouns or specific formats.
    • Classification: Labeling each found item with its matching category type.
    • Output: Presenting the findings in a structured format like a list or database table.

    Real-World Uses

    • Customer Support: Reading user emails to spot product names and route tickets to the correct team.
    • Search Engines: Tagging news articles so users can filter by exact company names or places.
    • Document Processing: Pulling totals, names, and dates straight off scanned invoices or legal contracts.

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    iWeb Scraping eliminates manual data entry with AI-powered extraction for businesses worldwide.

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