Why Semantic similarity with embeddings in NLP? - Purpose & Use Cases

The Big Idea

What if your computer could understand the meaning behind your words, not just the words themselves?

The Scenario

Imagine you have thousands of sentences and you want to find which ones mean the same thing. Doing this by reading and comparing each sentence one by one is like trying to find a friend in a huge crowd by calling their name loudly.

The Problem

Manually checking sentence meanings is slow and tiring. It's easy to miss subtle differences or similarities, and as the number of sentences grows, it quickly becomes impossible to keep track without mistakes.

The Solution

Semantic similarity with embeddings turns sentences into numbers that capture their meaning. This way, computers can quickly compare these numbers to find how close sentences are in meaning, making the search fast and accurate.

Before vs After

✗ Before

for s1 in sentences:
    for s2 in sentences:
        if s1 != s2 and s1 == s2:
            print('Similar:', s1, s2)

✓ After

embeddings = model.encode(sentences)
similarity = cosine_similarity([embeddings[0]], [embeddings[1]])
print('Similarity score:', similarity[0][0])

What It Enables

This lets us quickly find and group sentences or texts that mean the same thing, even if they use different words.

Real Life Example

When you search for a product review, semantic similarity helps find reviews that express the same opinion, even if they use different phrases, making your search smarter and more helpful.

Key Takeaways

Manual comparison of sentence meanings is slow and error-prone.

Embeddings convert text into numbers capturing meaning for fast comparison.

Semantic similarity enables smart, quick understanding of text relationships.