Sentiment Analysis on Amazon Reviews- Natural Language Processing

Embedding Layer in Natural Language Processing

Learning Outcome

5

Implement an Embedding layer using Keras

4

Differentiate between Random and Pre-trained embeddings

3

Describe how embedding lookup works

2

Explain why embeddings are preferred over one-hot encoding

1

Understand what an Embedding Layer is in NLP

Before this topic, we already learned:

After tokenization, how does a model understand meaning?

  1. Introduction to NLP
     
  2. Text preprocessing
     
  3. Tokenization
     
  4. Converting words into numbers

Imagine two ways to describe a person:

One-hot = Only their name in attendance register

Embedding = Full biography (interests, profession, behavior)

Just knowing a word’s position in vocabulary is not enough.
We want its story and relationships

In NLP, models cannot understand text directly...

This is called an Embedding Layer

Let's understand it in detail....

Instead:

Representing word as a position

We represent word as:

A dense vector of real numbers capturing meaning & context

What is an Embedding Layer?

Example:

Input token: 3


Output vector: [0.2, -0.1, 0.9, 0.7]

Converts words into dense vectors
 

Captures semantic meaning
 

Learns contextual relationships
 

Is trainable

Why is Embedding Layer Important?

Meaningful representations

Reduces dimensionality

Better Generation

Memory efficient

Captures semantic relationships between words

Compact vector representations instead of sparse one-hot

Dense vectors save storage space significantly

Helps deep learning models perform better

How It Works ?

TOKENIZE

Convert words into integers

LOOKUP

Integer maps to vector via embedding table

TRAIN

Vectors updated during backpropagation

Example:

Input tokens:[3, 7, 4]

Each maps to:4-dimensional vector

Types of Embeddings

RANDOM (TRAINABLE)

Created from scratch

 

Updated during training

 

Task-specific learning

PRE-TRAINED

Already trained on large corpora:

  1. Word2Vec

  2. GloVe

  3. fastText

Can be: Static or Fine-tuned

Embeddings         One-Hot Encoding

FeatureOne-HotEmbedding
Vector LengthVocabulary sizeFixed
Semantic Meaning❌✅
Memory Efficient​❌✅
Learns Context​❌✅

 Embedding Layer in Keras

Architecture:

input_dim

Vocabulary size

output_dim

Embedding vector dimension

input_length

Maximum sequence length

Implementation

model = Sequential([

Embedding(

input_dim=vocab_size,

output_dim=embed_dim,

input_length=max_len

),

Flatten(),

Dense(units)

])

Daily Life Applications

Summary

5

Widely used in NLP models

4

Can be random or pre-trained

3

More efficient than one-hot encoding

2

Captures semantic meaning & relationships

1

Embedding Layer converts words into dense vectors

Quiz

Which statement best explains why embeddings are preferred over one-hot encoding?

A) They increase vocabulary size

 B) They create sparse vectors

C) They capture semantic meaning in dense vectors

 D) They remove need for tokenization

Quiz-Answer

Which statement best explains why embeddings are preferred over one-hot encoding?

A) They increase vocabulary size

 B) They create sparse vectors

C) They capture semantic meaning in dense vectors

 D) They remove need for tokenization

Embedding Layer in Natural Language Processing

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Embedding Layer in Natural Language Processing

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