Why embeddings capture semantic meaning in NLP

Introduction

Embeddings turn words into numbers so computers can understand their meaning. They group similar words close together, showing their related ideas.

When you want a computer to understand the meaning of words in a sentence.

When building a search engine that finds similar documents or questions.

When creating a chatbot that needs to understand user intent.

When analyzing customer reviews to find common themes or feelings.

When translating languages by comparing word meanings.

Syntax

NLP

embedding = Embedding(input_dim, output_dim)
vector = embedding(word_index)

input_dim is the size of your vocabulary (number of unique words).

output_dim is the size of the vector that represents each word.

Examples

This creates a 50-dimensional vector for the word with index 42 in a vocabulary of 10,000 words.

NLP

embedding = Embedding(10000, 50)
vector = embedding(42)

This creates a 100-dimensional vector for the word with index 7 in a vocabulary of 5,000 words.

NLP

embedding = Embedding(5000, 100)
vector = embedding(7)

Sample Model

This code shows how embeddings represent words as vectors. It calculates similarity between related words. 'cat' and 'dog' are animals, so their vectors are closer. 'apple' and 'orange' are fruits, so their vectors are also close.

NLP

import numpy as np

# Simple example of word embeddings using random vectors
vocab = ['cat', 'dog', 'apple', 'orange']

# Assign random 3D vectors to each word
embeddings = {
    'cat': np.array([0.9, 0.1, 0.3]),
    'dog': np.array([0.8, 0.2, 0.4]),
    'apple': np.array([0.1, 0.9, 0.7]),
    'orange': np.array([0.2, 0.8, 0.6])
}

# Function to find similarity (cosine similarity)
def cosine_similarity(vec1, vec2):
    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))

# Compare similarity between 'cat' and 'dog'
sim_cat_dog = cosine_similarity(embeddings['cat'], embeddings['dog'])
# Compare similarity between 'apple' and 'orange'
sim_apple_orange = cosine_similarity(embeddings['apple'], embeddings['orange'])

print(f"Similarity between 'cat' and 'dog': {sim_cat_dog:.2f}")
print(f"Similarity between 'apple' and 'orange': {sim_apple_orange:.2f}")

Important Notes

Embeddings capture meaning because similar words appear in similar contexts, so their vectors become close.

Training embeddings on lots of text helps the model learn these relationships automatically.

Cosine similarity is a common way to measure how close two word vectors are.

Summary

Embeddings turn words into numbers that show their meaning.

Words with similar meanings have vectors close together.

This helps computers understand language better.

Practice

1. Why do word embeddings help computers understand language better?

easy

A. Because they turn words into numbers that show their meaning

B. Because they translate words into different languages

C. Because they count how many times a word appears

D. Because they remove stop words from sentences

Solution

1. Step 1: Understand what embeddings do

   Embeddings convert words into numbers (vectors) that represent their meanings.

2. Step 2: Recognize the benefit for computers

   These numbers help computers see which words are similar in meaning by their closeness in vector space.

3. Final Answer:

   Because they turn words into numbers that show their meaning -> Option A

4. Quick Check:

   Embeddings = numeric meaning representation [OK]

Hint: Embeddings = words as meaningful numbers [OK]

Common Mistakes:

• Thinking embeddings translate languages
• Confusing embeddings with word frequency counts
• Believing embeddings remove words

2. Which of the following is the correct way to represent a word embedding vector in code?

easy

A. embedding = 'word vector'

B. embedding = {'word': 1}

C. embedding = 12345

D. embedding = [0.1, 0.5, -0.3]

Solution

1. Step 1: Identify the data type for embeddings

   Embeddings are numeric vectors, usually lists or arrays of floats.

2. Step 2: Check each option's format

   embedding = [0.1, 0.5, -0.3] shows a list of numbers, which is correct. Others are strings, integers, or dictionaries, which are incorrect.

3. Final Answer:

   embedding = [0.1, 0.5, -0.3] -> Option D

4. Quick Check:

   Embedding vector = list of numbers [OK]

Hint: Embedding = list of numbers, not strings or ints [OK]

Common Mistakes:

• Using strings instead of numeric vectors
• Using single numbers instead of vectors
• Using dictionaries instead of lists

3. Given the following embeddings:
embedding_cat = [0.2, 0.4, 0.6]
embedding_dog = [0.21, 0.39, 0.58]
embedding_car = [0.9, 0.1, 0.2]
Which pair is most semantically similar based on cosine similarity?

medium

A. dog and car

B. cat and car

C. cat and dog

D. All pairs are equally similar

Solution

1. Step 1: Understand cosine similarity

   Cosine similarity measures how close two vectors point in the same direction; higher means more similar.

2. Step 2: Compare vectors

   embedding_cat and embedding_dog are close numerically, so their cosine similarity is high. embedding_car is quite different.

3. Final Answer:

   cat and dog -> Option C

4. Quick Check:

   Closest vectors = most similar words [OK]

Hint: Closest vectors mean similar words [OK]

Common Mistakes:

• Assuming car is similar to cat or dog
• Thinking all pairs have same similarity
• Ignoring vector closeness

4. You have this code snippet to compute similarity between two embeddings:

def similarity(vec1, vec2):
    return sum(a*b for a, b in zip(vec1, vec2))

embedding1 = [0.3, 0.5, 0.2]
embedding2 = [0.3, 0.5]
print(similarity(embedding1, embedding2))

What is the main problem here?

medium

A. The vectors have different lengths causing incorrect similarity

B. The function uses sum instead of product

C. The function should return a list, not a number

D. The embeddings contain strings instead of numbers

Solution

1. Step 1: Check vector lengths

   embedding1 has 3 elements, embedding2 has 2 elements, so zip stops early, ignoring last element of embedding1.

2. Step 2: Understand impact on similarity

   This causes incomplete calculation and inaccurate similarity score.

3. Final Answer:

   The vectors have different lengths causing incorrect similarity -> Option A

4. Quick Check:

   Vector length mismatch = wrong similarity [OK]

Hint: Vectors must be same length for similarity [OK]

Common Mistakes:

• Ignoring vector length mismatch
• Thinking sum is wrong operation here
• Expecting list output instead of number

5. You want to improve a chatbot's understanding by using embeddings. Which approach best captures semantic meaning for similar questions like "How are you?" and "How do you do?"?

hard

A. Use only the first word's embedding as sentence meaning

B. Use pretrained word embeddings and average their vectors for the whole sentence

C. Use random vectors for each word without training

D. Use one-hot encoding for each word and sum them

Solution

1. Step 1: Understand sentence embedding from word embeddings

   Averaging pretrained word embeddings creates a vector representing the whole sentence's meaning.

2. Step 2: Compare other options

   One-hot encoding loses semantic info, random vectors have no meaning, and using only first word misses context.

3. Final Answer:

   Use pretrained word embeddings and average their vectors for the whole sentence -> Option B

4. Quick Check:

   Average pretrained embeddings = better sentence meaning [OK]

Hint: Average pretrained embeddings for sentence meaning [OK]

Common Mistakes:

• Using one-hot encoding which lacks meaning
• Using random vectors without training
• Ignoring all words except the first