Stemming (Porter, Snowball) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Porter stemmer object.

NLP

from nltk.stem import [1]
porter = [1]()

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

APorterStemmer

BSnowballStemmer

CWordNetLemmatizer

DLancasterStemmer

Attempts:

3 left

💡 Hint

Common Mistakes

Using SnowballStemmer instead of PorterStemmer

Trying to import WordNetLemmatizer which is for lemmatization, not stemming

The PorterStemmer class from nltk.stem is used to create a Porter stemmer object.

2fill in blank

medium

Complete the code to stem the word 'running' using the Porter stemmer.

NLP

word = 'running'
stemmed_word = porter.[1](word)
print(stemmed_word)

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

Atokenize

Bstem

Clemmatize

Dparse

Attempts:

3 left

💡 Hint

Common Mistakes

Using lemmatize() method which is not available in PorterStemmer

Using tokenize() or parse() which are unrelated to stemming

The stem() method is used to get the stem of a word using the Porter stemmer.

3fill in blank

hard

Fix the error in the code to create a Snowball stemmer for English.

NLP

from nltk.stem import SnowballStemmer
stemmer = SnowballStemmer('[1]')

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

Agerman

Bspanish

Cfrench

Denglish

Attempts:

3 left

💡 Hint

Common Mistakes

Using capitalized language names like 'English'

Using a language different from the intended one

The SnowballStemmer requires the language name as a lowercase string. For English, use 'english'.

4fill in blank

hard

Fill both blanks to stem the word 'happiness' using SnowballStemmer for English.

NLP

from nltk.stem import [1]
stemmer = [1]('[2]')
print(stemmer.stem('happiness'))

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

ASnowballStemmer

BPorterStemmer

Cenglish

Dspanish

Attempts:

3 left

💡 Hint

Common Mistakes

Using PorterStemmer with a language argument (it does not take one)

Using 'spanish' instead of 'english' for the language

Use SnowballStemmer and specify the language as 'english' to stem English words.

5fill in blank

hard

Fill all three blanks to create a dictionary of stemmed words from a list using PorterStemmer.

NLP

from nltk.stem import [1]
porter = [1]()
words = ['running', 'jumps', 'easily']
stemmed = {word: porter.[2](word) for word in [3]
print(stemmed)

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

APorterStemmer

Bstem

Cwords

DSnowballStemmer

Attempts:

3 left

💡 Hint

Common Mistakes

Using SnowballStemmer instead of PorterStemmer

Using lemmatize() instead of stem()

Using a variable name other than words in the loop

Import and create a PorterStemmer object, then use its stem() method to stem each word in the list words.

Practice

1. What is the main purpose of stemming in Natural Language Processing?

easy

A. To reduce words to their base or root form

B. To translate text into another language

C. To count the number of words in a sentence

D. To generate synonyms for words

Solution

1. Step 1: Understand stemming concept

   Stemming simplifies words by cutting off suffixes to get the root form.

2. Step 2: Compare options with stemming goal

   Only To reduce words to their base or root form describes reducing words to their base form, which is the goal of stemming.

3. Final Answer:

   To reduce words to their base or root form -> Option A

4. Quick Check:

   Stemming = base form reduction [OK]

Hint: Stemming cuts word endings to find the root [OK]

Common Mistakes:

• Confusing stemming with translation
• Thinking stemming counts words
• Mixing stemming with synonym generation

2. Which of the following is the correct way to import the Porter Stemmer from NLTK in Python?

easy

A. from nltk.stem import PorterStemmer

B. import nltk.PorterStemmer

C. from nltk import PorterStemmer

D. import PorterStemmer from nltk.stem

Solution

1. Step 1: Recall correct import syntax in Python

   Python imports use 'from module import class' format for specific classes.

2. Step 2: Match with NLTK Porter Stemmer import

   The correct import is 'from nltk.stem import PorterStemmer' as it imports the class from the stem module.

3. Final Answer:

   from nltk.stem import PorterStemmer -> Option A

4. Quick Check:

   Correct import uses 'from nltk.stem import PorterStemmer' [OK]

Hint: Use 'from module import class' for specific imports [OK]

Common Mistakes:

• Using dot notation incorrectly in import
• Trying to import class directly from nltk
• Wrong order of import keywords

3. What is the output of the following Python code using Porter Stemmer?

from nltk.stem import PorterStemmer
ps = PorterStemmer()
words = ['running', 'runs', 'runner']
stemmed = [ps.stem(word) for word in words]
print(stemmed)

medium

A. ['run', 'run', 'run']

B. ['running', 'runs', 'runner']

C. ['run', 'run', 'runner']

D. ['runn', 'run', 'runn']

Solution

1. Step 1: Apply Porter Stemmer to each word

   Porter Stemmer reduces 'running' and 'runs' to 'run', but 'runner' remains 'runner' because it is treated differently.

2. Step 2: List the stemmed results

   The list becomes ['run', 'run', 'runner'] after stemming.

3. Final Answer:

   ['run', 'run', 'runner'] -> Option C

4. Quick Check:

   Porter stems 'running' and 'runs' to 'run' [OK]

Hint: Porter stems common verb forms to root, but some nouns stay [OK]

Common Mistakes:

• Assuming all words stem to the same root
• Confusing stemmed output with original words
• Expecting 'runner' to stem to 'run'

4. Identify the error in this Snowball Stemmer usage code snippet:

from nltk.stem import SnowballStemmer
stemmer = SnowballStemmer('english')
words = ['happiness', 'happier', 'happy']
stemmed_words = [stemmer.stem(word) for word in words]
print(stemmed_words)

medium

A. The stem method should be called as stemmer.stem_word(word)

B. No error; code runs correctly and prints stemmed words

C. SnowballStemmer requires language name in uppercase

D. SnowballStemmer must be imported from nltk.stem.snowball

Solution

1. Step 1: Check SnowballStemmer import and usage

   Importing from nltk.stem and initializing with 'english' is correct and case-insensitive.

2. Step 2: Verify method call and output

   The stem method is correctly called as stemmer.stem(word), and the code prints stemmed words without error.

3. Final Answer:

   No error; code runs correctly and prints stemmed words -> Option B

4. Quick Check:

   SnowballStemmer usage is correct as shown [OK]

Hint: SnowballStemmer language is lowercase string, stem() method used [OK]

Common Mistakes:

• Using uppercase language name incorrectly
• Calling non-existent stem_word method
• Wrong import path for SnowballStemmer

5. You want to preprocess text data by stemming words but keep the original word if it is shorter than 4 characters. Which Python code snippet using Porter Stemmer correctly implements this?

hard

A. stemmed = [ps.stem(word) for word in words if len(word) >= 4]

B. stemmed = [ps.stem(word) if len(word) < 4 else word for word in words]

C. stemmed = [word for word in words if len(word) < 4 else ps.stem(word)]

D. stemmed = [word if len(word) < 4 else ps.stem(word) for word in words]

Solution

1. Step 1: Understand the condition for stemming

   Words shorter than 4 characters should remain unchanged; others should be stemmed.

2. Step 2: Check list comprehension syntax

   stemmed = [word if len(word) < 4 else ps.stem(word) for word in words] uses correct if-else inside list comprehension: 'word if len(word) < 4 else ps.stem(word)'.

3. Final Answer:

   stemmed = [word if len(word) < 4 else ps.stem(word) for word in words] -> Option D

4. Quick Check:

   Keep short words, stem others with if-else [OK]

Hint: Use 'word if condition else stem(word)' in list comprehension [OK]

Common Mistakes:

• Swapping if-else order in comprehension
• Using if without else causing missing elements
• Incorrect syntax mixing if-else and for