Text preprocessing (tokenization, stemming, lemmatization) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tokenize the sentence into words using NLTK.

ML Python

from nltk.tokenize import word_tokenize
sentence = "I love learning AI!"
tokens = [1](sentence)
print(tokens)

Drag options to blanks, or click blank then click option'

Asplit_words

Bsent_tokenize

Ctokenize_words

Dword_tokenize

Attempts:

3 left

💡 Hint

Common Mistakes

Using sent_tokenize which splits text into sentences instead of words.

The word_tokenize function splits a sentence into individual words (tokens).

2fill in blank

medium

Complete the code to stem the word using PorterStemmer.

ML Python

from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
word = "running"
stemmed_word = stemmer.[1](word)
print(stemmed_word)

Drag options to blanks, or click blank then click option'

Alemmatize

Bsplit

Cstem

Dtokenize

Attempts:

3 left

💡 Hint

Common Mistakes

Using lemmatize which is a different method for word normalization.

The stem method reduces a word to its root form by chopping off suffixes.

3fill in blank

hard

Fix the error in the code to lemmatize the word correctly using WordNetLemmatizer.

ML Python

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
word = "better"
lemma = lemmatizer.[1](word, pos='a')
print(lemma)

Drag options to blanks, or click blank then click option'

Alemmatize

Bstem

Ctokenize

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using stem method which does not consider part of speech.

The lemmatize method returns the base form of a word considering its part of speech.

4fill in blank

hard

Fill both blanks to create a dictionary of word stems for words longer than 4 characters.

ML Python

from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
words = ['running', 'jumps', 'easily', 'fairly']
stem_dict = {word: [1] for word in words if len(word) [2] 4}
print(stem_dict)

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Astemmer.stem(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its stem.

Using '<=' instead of '>' in the condition.

The dictionary comprehension applies stemmer.stem(word) to words with length greater than 4.

5fill in blank

hard

Fill both blanks to create a dictionary of lemmas for words longer than 5 characters.

ML Python

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
words = ['running', 'jumps', 'easily', 'fairly']
lemma_dict = {word: lemmatizer.[1](word, pos='r') for word in words if len(word) [2] 5}
print(lemma_dict)

Drag options to blanks, or click blank then click option'

Blemmatize

C>

Dstem

Attempts:

3 left

💡 Hint

Common Mistakes

Adding extra characters in the key part of the dictionary.

Using stem instead of lemmatize.

Using '<' instead of '>' in the condition.

The dictionary comprehension maps each word to its lemma using lemmatize for words longer than 5 characters. The first blank is empty because the key is just the word itself.

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