Text preprocessing (tokenization, stemming, lemmatization) in ML Python

Introduction

Text preprocessing helps computers understand and work with words better by breaking text into pieces and simplifying words.

When you want to prepare text data for a chatbot to understand user messages.

When analyzing customer reviews to find common opinions or feelings.

When building a search engine that finds documents based on keywords.

When cleaning up text data before training a machine learning model.

When summarizing large articles by focusing on important words.

Syntax

ML Python

from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer

# Tokenization
words = word_tokenize(text)

# Stemming
stemmer = PorterStemmer()
stemmed_words = [stemmer.stem(word) for word in words]

# Lemmatization
lemmatizer = WordNetLemmatizer()
lemmatized_words = [lemmatizer.lemmatize(word) for word in words]

Tokenization splits text into words or pieces.

Stemming cuts words to their root form, which may not be a real word.

Examples

This splits the sentence into individual words and punctuation.

ML Python

text = "Cats are running faster than dogs."
words = word_tokenize(text)
print(words)

This reduces words to their stem forms, like 'running' to 'run'.

ML Python

stemmer = PorterStemmer()
stemmed = [stemmer.stem(word) for word in words]
print(stemmed)

This converts words to their dictionary form, like 'running' to 'running' or 'ran' to 'run'.

ML Python

lemmatizer = WordNetLemmatizer()
lemmas = [lemmatizer.lemmatize(word) for word in words]
print(lemmas)

Sample Model

This program shows how to split text into words, then simplify those words by stemming and lemmatization.

ML Python

import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer

text = "The cats are running faster than the dogs."

# Tokenize
words = word_tokenize(text)
print("Tokens:", words)

# Stem
stemmer = PorterStemmer()
stemmed_words = [stemmer.stem(word) for word in words]
print("Stemmed:", stemmed_words)

# Lemmatize
lemmatizer = WordNetLemmatizer()
lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
print("Lemmatized:", lemmatized_words)

Important Notes

Stemming may produce words that are not real, while lemmatization produces real dictionary words.

Lemmatization needs the word's part of speech to be most accurate, but defaults still help.

Tokenization handles punctuation and splits text into manageable pieces.

Summary

Tokenization breaks text into words or tokens.

Stemming cuts words to their root but may not keep real words.

Lemmatization changes words to their dictionary form for better understanding.

Practice

1. What is the main purpose of tokenization in text preprocessing?

easy

A. To split text into smaller units like words or tokens

B. To convert words to their base dictionary form

C. To remove stop words from the text

D. To reduce words to their root form by cutting suffixes

Solution

1. Step 1: Understand tokenization

   Tokenization means breaking text into smaller pieces called tokens, usually words.

2. Step 2: Compare with other preprocessing steps

   Lemmatization and stemming change words, but tokenization just splits text.

3. Final Answer:

   To split text into smaller units like words or tokens -> Option A

4. Quick Check:

   Tokenization = splitting text [OK]

Hint: Tokenization means splitting text into words or tokens [OK]

Common Mistakes:

• Confusing tokenization with stemming or lemmatization
• Thinking tokenization changes word forms
• Assuming tokenization removes words

2. Which of the following Python code snippets correctly performs stemming using NLTK's PorterStemmer?

easy

A. from nltk.tokenize import word_tokenize
word_tokenize('running')

B. import re
re.sub(r'\W+', '', 'running')

C. from nltk.stem import WordNetLemmatizer
wl = WordNetLemmatizer()
wl.lemmatize('running')

D. from nltk.stem import PorterStemmer
ps = PorterStemmer()
ps.stem('running')

Solution

1. Step 1: Identify stemming code

   PorterStemmer from nltk.stem is used for stemming; calling stem('running') returns stemmed word.

2. Step 2: Check other options

   from nltk.tokenize import word_tokenize word_tokenize('running') is tokenization, C is lemmatization, D is regex substitution, not stemming.

3. Final Answer:

   from nltk.stem import PorterStemmer ps = PorterStemmer() ps.stem('running') -> Option D

4. Quick Check:

   PorterStemmer.stem() = stemming [OK]

Hint: PorterStemmer.stem() is for stemming, not tokenization or lemmatization [OK]

Common Mistakes:

• Confusing stemming with lemmatization functions
• Using tokenization code for stemming
• Using regex instead of stemmer

3. What will be the output of the following Python code using NLTK's WordNetLemmatizer?

from nltk.stem import WordNetLemmatizer
wl = WordNetLemmatizer()
print(wl.lemmatize('better', pos='a'))

medium

A. good

B. bet

C. better

D. betters

Solution

1. Step 1: Understand lemmatization with POS

   Using pos='a' means adjective. Lemmatizer converts 'better' (comparative adjective) to base form 'good'.

2. Step 2: Check output possibilities

   Stemming would cut to 'bet', but lemmatization returns dictionary base word 'good'.

3. Final Answer:

   good -> Option A

4. Quick Check:

   Lemmatize('better', pos='a') = 'good' [OK]

Hint: Lemmatizer with pos='a' returns base adjective form [OK]

Common Mistakes:

• Expecting stemming output instead of lemmatization
• Ignoring pos parameter in lemmatization
• Assuming output is unchanged word

4. Identify the error in this code snippet for stemming a list of words using NLTK's PorterStemmer:

from nltk.stem import PorterStemmer
ps = PorterStemmer()
words = ['running', 'jumps', 'easily']
stemmed = [ps.stem(words)]
print(stemmed)

medium

A. The list comprehension syntax is incorrect

B. PorterStemmer is not imported correctly

C. ps.stem() expects a single word, but a list was passed

D. The print statement is missing parentheses

Solution

1. Step 1: Check ps.stem() input

   ps.stem() expects a single string word, but words (a list) was passed directly.

2. Step 2: Identify correct usage

   We should stem each word individually, e.g., using list comprehension: [ps.stem(w) for w in words].

3. Final Answer:

   ps.stem() expects a single word, but a list was passed -> Option C

4. Quick Check:

   Stem() needs single word input [OK]

Hint: Stem words one by one, not as a whole list [OK]

Common Mistakes:

• Passing list directly to stem() instead of iterating
• Confusing stemming with tokenization
• Incorrect list comprehension syntax

5. You want to preprocess text data by first splitting sentences into words, then reducing words to their dictionary base form, and finally removing suffixes to get root forms. Which sequence of steps is correct?

hard

A. Tokenization -> Lemmatization -> Stemming

B. Tokenization -> Stemming -> Lemmatization

C. Lemmatization -> Stemming -> Tokenization

D. Stemming -> Tokenization -> Lemmatization

Solution

1. Step 1: Understand preprocessing order

   First, split text into tokens (words) using tokenization.

2. Step 2: Apply stemming then lemmatization

   Stemming reduces words to root forms by removing suffixes; lemmatization converts words to dictionary base form.

3. Step 3: Verify option sequence

   Tokenization -> Stemming -> Lemmatization matches the correct order: Tokenization -> Stemming -> Lemmatization.

4. Final Answer:

   Tokenization -> Stemming -> Lemmatization -> Option B

5. Quick Check:

   Tokenize first, then stem, then lemmatize [OK]

Hint: Tokenize first, then stem, then lemmatize for best results [OK]

Common Mistakes:

• Applying stemming before tokenization
• Skipping tokenization step
• Confusing order of lemmatization and stemming