Semantic similarity with embeddings in NLP

Introduction

Semantic similarity helps us find how close two pieces of text are in meaning, even if they use different words.

Finding if two sentences mean the same thing in a chatbot.

Grouping similar customer reviews together.

Recommending articles that talk about similar topics.

Checking if a question is already answered in a FAQ.

Matching job descriptions with resumes.

Syntax

NLP

embedding1 = model.encode(text1)
embedding2 = model.encode(text2)
similarity = cosine_similarity([embedding1], [embedding2])[0][0]

Use a pre-trained model to convert text into embeddings (numbers).

Cosine similarity measures how close two embeddings are, from -1 (opposite) to 1 (same).

Examples

Compare two fruit-related sentences to see how similar they are.

NLP

embedding1 = model.encode('I love apples')
embedding2 = model.encode('I like oranges')
similarity = cosine_similarity([embedding1], [embedding2])[0][0]

Check similarity between two sentences about pets and places.

NLP

embedding1 = model.encode('The cat sits on the mat')
embedding2 = model.encode('A dog lies on the rug')
similarity = cosine_similarity([embedding1], [embedding2])[0][0]

Sample Model

This program uses a pre-trained model to turn sentences into numbers and then finds how close their meanings are using cosine similarity.

NLP

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Load a small pre-trained model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Two example sentences
text1 = 'I enjoy reading books about history.'
text2 = 'Books on historical topics are my favorite.'

# Get embeddings
embedding1 = model.encode(text1)
embedding2 = model.encode(text2)

# Compute cosine similarity
similarity = cosine_similarity([embedding1], [embedding2])[0][0]

print(f'Semantic similarity: {similarity:.4f}')

Important Notes

Embeddings capture meaning beyond exact words, so synonyms get high similarity.

Cosine similarity close to 1 means very similar; close to 0 means unrelated.

Pre-trained models like 'all-MiniLM-L6-v2' are fast and good for many tasks.

Summary

Semantic similarity uses embeddings to compare meanings of text.

Cosine similarity measures how close two embeddings are.

Pre-trained models make it easy to get embeddings for sentences.

Practice

1. What does semantic similarity with embeddings help us do in natural language processing?

easy

A. Translate text from one language to another

B. Count the number of words in a sentence

C. Measure how similar the meanings of two texts are

D. Generate random sentences

Solution

1. Step 1: Understand semantic similarity

   Semantic similarity means checking how close the meanings of two texts are, not just the words.

2. Step 2: Role of embeddings

   Embeddings convert text into numbers that capture meaning, allowing comparison of texts by meaning.

3. Final Answer:

   Measure how similar the meanings of two texts are -> Option C

4. Quick Check:

   Semantic similarity = meaning comparison [OK]

Hint: Semantic similarity compares meanings, not word counts [OK]

Common Mistakes:

• Confusing similarity with word count
• Thinking embeddings translate text
• Assuming semantic similarity generates text

2. Which Python library is commonly used to compute cosine similarity between embeddings?

easy

A. matplotlib

B. scikit-learn

C. pandas

D. flask

Solution

1. Step 1: Identify cosine similarity function

   Cosine similarity is often computed using scikit-learn's metrics module.

2. Step 2: Check other libraries

   matplotlib is for plotting, pandas for data frames, flask for web apps, so they don't compute cosine similarity.

3. Final Answer:

   scikit-learn -> Option B

4. Quick Check:

   Cosine similarity = scikit-learn [OK]

Hint: Use scikit-learn for cosine similarity calculations [OK]

Common Mistakes:

• Using matplotlib for similarity
• Confusing pandas with similarity tools
• Thinking flask handles embeddings

3. What is the output of this Python code snippet?

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

emb1 = np.array([[1, 0, 0]])
emb2 = np.array([[0, 1, 0]])
sim = cosine_similarity(emb1, emb2)
print(sim[0][0])

medium

A. Error

B. 1.0

C. -1.0

D. 0.0

Solution

1. Step 1: Understand cosine similarity formula

   Cosine similarity measures the cosine of the angle between two vectors. Orthogonal vectors have similarity 0.

2. Step 2: Analyze given vectors

   emb1 is [1,0,0], emb2 is [0,1,0]. They are perpendicular, so similarity is 0.

3. Final Answer:

   0.0 -> Option D

4. Quick Check:

   Orthogonal vectors similarity = 0.0 [OK]

Hint: Orthogonal vectors have cosine similarity zero [OK]

Common Mistakes:

• Assuming similarity is 1 for any vectors
• Confusing dot product with cosine similarity
• Expecting error due to shape

4. Identify the error in this code that tries to compute semantic similarity:

from sklearn.metrics.pairwise import cosine_similarity

emb1 = [0.1, 0.2, 0.3]
emb2 = [0.1, 0.2, 0.3]
sim = cosine_similarity(emb1, emb2)
print(sim)

medium

A. emb1 and emb2 should be 2D arrays, not 1D lists

B. cosine_similarity function does not exist in sklearn

C. embeddings must be strings, not numbers

D. print statement syntax is incorrect

Solution

1. Step 1: Check input format for cosine_similarity

   cosine_similarity expects 2D arrays (like [[...]]), but emb1 and emb2 are 1D lists.

2. Step 2: Confirm other options

   cosine_similarity exists, embeddings are numeric vectors, and print syntax is correct in Python 3.

3. Final Answer:

   emb1 and emb2 should be 2D arrays, not 1D lists -> Option A

4. Quick Check:

   Input shape must be 2D arrays [OK]

Hint: cosine_similarity needs 2D arrays, not 1D lists [OK]

Common Mistakes:

• Passing 1D lists instead of 2D arrays
• Thinking embeddings must be text
• Misunderstanding print syntax

5. You have two sentences: "I love apples" and "I adore oranges". Using a pre-trained embedding model, you get vectors for both. Which approach best helps you find if these sentences have similar meaning?

hard

A. Calculate cosine similarity between their embeddings

B. Count common words between the sentences

C. Check if sentence lengths are equal

D. Compare the first letters of each word

Solution

1. Step 1: Understand semantic similarity goal

   We want to compare meanings, not just words or sentence length.

2. Step 2: Use embeddings and cosine similarity

   Pre-trained embeddings capture meaning; cosine similarity measures closeness of meanings numerically.

3. Final Answer:

   Calculate cosine similarity between their embeddings -> Option A

4. Quick Check:

   Meaning comparison = cosine similarity on embeddings [OK]

Hint: Use cosine similarity on embeddings for meaning comparison [OK]

Common Mistakes:

• Relying on word overlap only
• Using sentence length as similarity
• Comparing letters instead of meaning