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Information Extraction in NLP: Techniques, Tools & Real-World Examples

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Ever wondered how search engines pull facts from millions of documents or how chatbots recognize names, dates, and numbers in your messages? That’s the magic of information extraction in NLP. It’s the process of transforming unstructured text into structured, actionable data a core part of modern AI systems.

Task

Let's do a task ask ChatGPT about yourself.

What is Text Extraction Information?

As name suggested it will extract information from unstructured or semi-structured text. There many technique is used to identify entity, entity name, action and event. It gives standardize format which is stored in rows and column.

Text Extraction Process

  1. Name Entity Recognition:- It is information extraction task to identify name, organisation, date in unstructured text.

  2. Relation Extraction:- It basically say the relation between entity and data source.

  3. Event Extraction:- It recognise the action needs to be done. Like appointment or meeting.

  4. Sentiment Analysis:- As name suggested sentiment, the feeling behind the sentence. Well feeling are abstract we just feel don't see but in this technique the model identify according to your word which you have written.

"My order of a Samsung Galaxy S23 from your Seattle warehouse hasn’t arrived yet, and it was supposed to be delivered by July 10, 2025."

Example Text Extraction Process:

  1. Input Text: The customer’s message.

  2. Named Entity Recognition (NER): The NLP system identifies:

    • Product: Samsung Galaxy S23
    • Location: Seattle
    • Date: July 10, 2025

  3. Keyword Extraction: Identifies key terms like “order,” “delivered,” and “warehouse” to understand the context.

  4. Relation Extraction: Detects the relationship between “order” and “hasn’t arrived” to flag a delivery issue.

  5. Output: Structured data:
    {Product: "Samsung Galaxy S23", Location: "Seattle", Delivery Date: "July 10, 2025", Issue: "Non-delivered"}

  6. Application: The chatbot uses this data to:

    • Query the order database for the specific product and delivery status.
    • Respond with: “I’m sorry, it seems your Samsung Galaxy S23 order from our Seattle warehouse is delayed. Let me check the status and provide an update.”

  7. Sentiment here Negative.

Real-World Impact: This extraction enables the chatbot to quickly understand and address the customer’s issue, improving response time and user satisfaction. It’s used in customer support, logistics tracking, and automated ticketing systems.

Learn Text Extraction Basic:

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