One-hot encoding for text in NLP

Introduction

One-hot encoding turns words into simple lists of zeros and ones. This helps computers understand text by showing which words appear.

When you want to convert words into numbers for a machine learning model.

When you have a small set of words and want a simple way to represent them.

When you need to prepare text data for basic classification tasks.

When you want to check if certain words appear in a sentence.

When you want to compare texts by their word presence.

Syntax

NLP

from sklearn.preprocessing import OneHotEncoder

# Create encoder
encoder = OneHotEncoder(sparse_output=False)

# Fit and transform text data
encoded = encoder.fit_transform(text_data)

Input text data must be reshaped to 2D array like [['word1'], ['word2'], ...]

Output is a 2D array where each row is a one-hot vector for a word.

Examples

This example encodes a list of words. Each unique word gets a position with 1, others 0.

NLP

from sklearn.preprocessing import OneHotEncoder

words = [['cat'], ['dog'], ['cat'], ['bird']]
encoder = OneHotEncoder(sparse_output=False)
encoded = encoder.fit_transform(words)
print(encoded)

Shows the unique words found by the encoder.

NLP

from sklearn.preprocessing import OneHotEncoder

words = [['apple'], ['banana'], ['apple'], ['orange']]
encoder = OneHotEncoder(sparse_output=False)
encoded = encoder.fit_transform(words)
print(encoder.categories_)

Sample Model

This program shows how to convert a list of words into one-hot vectors. It prints the unique words and their encoded forms.

NLP

from sklearn.preprocessing import OneHotEncoder

# List of words to encode
words = [['hello'], ['world'], ['hello'], ['machine'], ['learning']]

# Create the encoder
encoder = OneHotEncoder(sparse_output=False)

# Fit and transform the words
encoded_words = encoder.fit_transform(words)

# Print the unique words found
print('Unique words:', encoder.categories_)

# Print the one-hot encoded vectors
print('One-hot encoded vectors:')
for word, vector in zip(words, encoded_words):
    print(f'{word[0]}: {vector}')

Important Notes

One-hot encoding creates very sparse vectors if you have many unique words.

For large text data, other methods like word embeddings are more efficient.

Always reshape your text data to 2D before using OneHotEncoder.

Summary

One-hot encoding turns words into simple vectors with one '1' and rest '0's.

It helps machine learning models understand which words appear.

Best for small sets of words or simple text tasks.

Practice

1. What does one-hot encoding do to words in text processing?

easy

A. Converts each word into a vector with one 1 and rest 0s

B. Replaces words with their synonyms

C. Counts the number of letters in each word

D. Sorts words alphabetically

Solution

1. Step 1: Understand one-hot encoding concept

   One-hot encoding creates a vector for each word where only one position is 1 and all others are 0.

2. Step 2: Compare options with definition

   Only Converts each word into a vector with one 1 and rest 0s matches this definition exactly.

3. Final Answer:

   Converts each word into a vector with one 1 and rest 0s -> Option A

4. Quick Check:

   One-hot encoding = vector with single 1 [OK]

Hint: One-hot means one 1 in vector, rest zeros [OK]

Common Mistakes:

• Thinking it replaces words with synonyms
• Confusing with counting letters
• Assuming it sorts words

2. Which of the following is the correct Python syntax to create a one-hot vector for the word 'cat' from vocabulary ['cat', 'dog', 'bird']?

easy

A. one_hot = [0, 0, 1]

B. one_hot = [0, 1, 0]

C. one_hot = [1, 1, 0]

D. one_hot = [1, 0, 0]

Solution

1. Step 1: Identify the index of 'cat' in vocabulary

   'cat' is at index 0 in ['cat', 'dog', 'bird'].

2. Step 2: Create one-hot vector with 1 at index 0

   The vector should have 1 at position 0 and 0 elsewhere: [1, 0, 0].

3. Final Answer:

   [1, 0, 0] -> Option D

4. Quick Check:

   Index 0 gets 1 in one-hot vector [OK]

Hint: Index of word = position of 1 in vector [OK]

Common Mistakes:

• Putting 1 in wrong index
• Using multiple 1s in vector
• Confusing word order in vocabulary

3. What will be the output of this Python code?

vocab = ['apple', 'banana', 'cherry']
word = 'banana'
one_hot = [1 if w == word else 0 for w in vocab]
print(one_hot)

medium

A. [1, 0, 0]

B. [0, 1, 0]

C. [0, 0, 1]

D. [1, 1, 0]

Solution

1. Step 1: Understand list comprehension logic

   For each word in vocab, put 1 if it matches 'banana', else 0.

2. Step 2: Apply to vocab list

   'apple' != 'banana' -> 0, 'banana' == 'banana' -> 1, 'cherry' != 'banana' -> 0, so [0, 1, 0].

3. Final Answer:

   [0, 1, 0] -> Option B

4. Quick Check:

   Only 'banana' gets 1 in vector [OK]

Hint: Check which vocab word equals target word [OK]

Common Mistakes:

• Mixing up word positions
• Using 1 for all words
• Misreading list comprehension

4. Identify the error in this one-hot encoding code snippet:

vocab = ['red', 'green', 'blue']
word = 'green'
one_hot = [0 if w == word else 1 for w in vocab]
print(one_hot)

medium

A. The list comprehension syntax is invalid

B. The vocabulary list is missing a word

C. The condition is reversed; it should assign 1 when words match

D. The print statement syntax is incorrect

Solution

1. Step 1: Analyze the list comprehension condition

   It assigns 0 if word matches, else 1, which is opposite of one-hot logic.

2. Step 2: Correct logic for one-hot encoding

   One-hot should assign 1 when words match and 0 otherwise.

3. Final Answer:

   The condition is reversed; it should assign 1 when words match -> Option C

4. Quick Check:

   Match word -> 1, else 0 [OK]

Hint: One-hot sets 1 for match, not 0 [OK]

Common Mistakes:

• Reversing 0 and 1 in condition
• Assuming syntax error instead of logic error
• Ignoring correct vocabulary

5. Given a vocabulary ['sun', 'moon', 'star'] and a sentence 'moon star sun star', which one-hot encoded matrix correctly represents the sentence?

hard

A. [[0,1,0],[0,0,1],[1,0,0],[0,0,1]]

B. [[1,0,0],[0,1,0],[0,0,1],[0,1,0]]

C. [[0,0,1],[1,0,0],[0,1,0],[1,0,0]]

D. [[1,1,0],[0,0,1],[1,0,0],[0,0,1]]

Solution

1. Step 1: Map each word to its one-hot vector

   Vocabulary indices: 'sun'->0, 'moon'->1, 'star'->2. So 'moon'=[0,1,0], 'star'=[0,0,1], 'sun'=[1,0,0].

2. Step 2: Encode sentence words in order

   Sentence words: 'moon' -> [0,1,0], 'star' -> [0,0,1], 'sun' -> [1,0,0], 'star' -> [0,0,1].

3. Final Answer:

   [[0,1,0],[0,0,1],[1,0,0],[0,0,1]] -> Option A

4. Quick Check:

   Each word vector matches vocab index [OK]

Hint: Match word order and vocab index for vectors [OK]

Common Mistakes:

• Mixing word order in sentence
• Swapping indices of words
• Using vectors with multiple 1s