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Anubhav Sharma
Anubhav Sharma

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The Anubhav portal was launched in March 2015 at the behest of the Hon'ble Prime Minister for retiring government officials to leave a record of their experiences while in Govt service .

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1 Answers

Ravi Vishwakarma
Ravi Vishwakarma 19 Feb 2026
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Semantic Matching is an AI/NLP technique used to determine whether two pieces of text have the same meaning, even if they use different words.

In simple terms:
It matches meaning, not just exact words.

Ravi Vishwakarma

1. Simple Example

Keyword Matching (Old method)

Query: "buy mobile online"
Text: "purchase smartphone on internet"

Result → Not matched (words are different)

Semantic Matching (Modern AI)

It understands:

  • buy = purchase
  • mobile = smartphone
  • online = internet

Result → Matched because meaning is same

2. Definition

Semantic Matching is the process of comparing two texts based on their meaning rather than exact words.

3. How It Works (Conceptually)

AI converts text into numerical vectors (embeddings).

Then it measures similarity between them using:

  • Cosine similarity
  • Distance metrics
  • Deep learning models

Closer vectors = More similar meaning.

4. Types of Semantic Matching

1. Semantic Search

Finds results based on meaning.

Example:
Search: "How to reduce weight?"
Results: "Tips for losing fat" 

2. Question-Answer Matching

Matches user question with best answer.

Used in:

  • Chatbots
  • Customer support systems

3. Sentence Similarity

  • Checks if two sentences mean the same.

4. Document Matching

  • Compares large texts like resumes vs job descriptions.

5. Where Semantic Matching is Used

Very common in AI systems:

  • Google Search
  • ChatGPT
  • Recommendation engines
  • Voice assistants
  • Plagiarism detection
  • Spam filtering
  • E-commerce search

6. Semantic Matching vs Keyword Matching

Feature Keyword Matching Semantic Matching
Checks words Yes No
Understands meaning No Yes
Handles synonyms No Yes
Accuracy Low High

7. Technologies Used

Semantic matching is built using:

  • Word Embeddings (Word2Vec, GloVe)
  • TF-IDF (basic level)
  • BERT / Transformers
  • Vector databases
  • Deep learning models

8. One-Line Explanation

Semantic Matching = Finding similarity based on meaning, not exact words.

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