Limitations of classical methods in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple bag-of-words representation of text.

NLP

from sklearn.feature_extraction.text import CountVectorizer
texts = ['I love AI', 'AI loves me']
vectorizer = CountVectorizer()
X = vectorizer.[1](texts)

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

Afit_transform

Btransform

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit or transform separately causes errors or incomplete processing.

The fit_transform method learns the vocabulary and transforms the texts into vectors in one step.

2fill in blank

medium

Complete the code to remove stop words from the text using CountVectorizer.

NLP

vectorizer = CountVectorizer(stop_words=[1])
X = vectorizer.fit_transform(texts)

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

A'english'

BNone

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using True or False does not remove stop words automatically.

Setting stop_words='english' removes common English stop words automatically.

3fill in blank

hard

Fix the error in the code that tries to vectorize text but uses an incorrect method.

NLP

vectorizer = CountVectorizer()
X = vectorizer.[1](['sample text'])

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

Apredict

Bfit_transform

Ctransform

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict or fit_predict causes attribute errors.

The CountVectorizer does not have a predict or fit_predict method. Use fit_transform to learn and transform the text.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters words longer than 3 characters.

NLP

filtered_words = {word: len(word) for word in words if len(word) [1] 3 and word [2] 'the'}

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

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '==' changes the filter logic incorrectly.

The code keeps words longer than 3 characters and excludes the word 'the'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths, excluding short words.

NLP

result = [1]: [2] for w in words if len(w) [3] 4

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

Aw.upper()

Blen(w)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' includes short words, changing the result.

The comprehension maps uppercase words to their lengths, only for words longer than 4 characters.

Practice

1. Which of the following is a main limitation of classical NLP methods like bag-of-words?

easy

A. They ignore the order and context of words in a sentence.

B. They require very large datasets to work.

C. They always need deep neural networks to function.

D. They can understand sarcasm and irony easily.

Solution

1. Step 1: Understand classical NLP methods

   Classical methods like bag-of-words treat text as a collection of words without order or context.

2. Step 2: Identify the limitation

   This means they cannot capture meaning that depends on word order or surrounding words.

3. Final Answer:

   They ignore the order and context of words in a sentence. -> Option A

4. Quick Check:

   Classical methods miss context = C [OK]

Hint: Remember bag-of-words loses word order and context [OK]

Common Mistakes:

• Thinking classical methods need big data
• Believing classical methods use deep learning
• Assuming classical methods understand sarcasm

2. Which syntax correctly represents a classical method feature extraction for text using bag-of-words in Python?

easy

A. import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)

B. import tensorflow as tf
model = tf.keras.Sequential()

C. from nltk.tokenize import word_tokenize
words = word_tokenize(text)

D. from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

Solution

1. Step 1: Identify classical method for feature extraction

   Bag-of-words uses CountVectorizer from sklearn to convert text to word counts.

2. Step 2: Match syntax to bag-of-words

   from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() X = vectorizer.fit_transform(texts) shows correct import and usage of CountVectorizer for feature extraction.

3. Final Answer:

   from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() X = vectorizer.fit_transform(texts) -> Option D

4. Quick Check:

   CountVectorizer syntax = A [OK]

Hint: CountVectorizer is sklearn's bag-of-words tool [OK]

Common Mistakes:

• Confusing tokenization with feature extraction
• Using deep learning imports for classical methods
• Mixing spaCy usage with bag-of-words

3. Given this code using bag-of-words, what is the shape of the output matrix X if texts = ['I love AI', 'love AI']?

medium

A. (2, 4)

B. (3, 2)

C. (2, 3)

D. (4, 2)

Solution

1. Step 1: Count unique words in texts

   Texts are ['I love AI', 'love AI']. Lowercased tokens: 'i love ai', 'love ai'. Unique tokens: 'ai', 'i', 'love' = 3 words.

2. Step 2: Check CountVectorizer default behavior

   CountVectorizer lowercases and tokenizes. Number of samples is 2. So shape is (2, 3).

3. Final Answer:

   (2, 3) -> Option C

4. Quick Check:

   2 samples, 3 features = B [OK]

Hint: Count unique words for shape: rows=samples, cols=unique words [OK]

Common Mistakes:

• Counting words instead of unique tokens
• Mixing rows and columns in shape
• Ignoring case sensitivity

4. Identify the error in this classical NLP code snippet using CountVectorizer:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['Hello world', 'Hello']
vectorizer = CountVectorizer()
X = vectorizer.fit(texts)
print(X.toarray())

medium

A. fit() should be fit_transform() to get the matrix.

B. CountVectorizer cannot process lists of strings.

C. toarray() is not a method of the output.

D. Missing import for numpy.

Solution

1. Step 1: Check CountVectorizer usage

   fit() learns the vocabulary but does not transform texts to matrix. fit_transform() does both.

2. Step 2: Identify correct method to get matrix

   To get the document-term matrix, fit_transform() must be used. Using fit() alone returns the vectorizer object, which has no toarray() method.

3. Final Answer:

   fit() should be fit_transform() to get the matrix. -> Option A

4. Quick Check:

   fit_transform() needed for matrix [OK]

Hint: Use fit_transform() to get matrix, not just fit() [OK]

Common Mistakes:

• Using fit() instead of fit_transform()
• Assuming toarray() works on vectorizer
• Thinking CountVectorizer needs numpy import

5. Why might classical NLP methods like bag-of-words fail on sentiment analysis of complex sentences such as 'I don't think this movie was good'?

hard

A. They cannot tokenize contractions like "don't".

B. They treat words independently and miss negation and word order.

C. They always overfit on small datasets.

D. They require GPU acceleration to process negations.

Solution

1. Step 1: Understand classical method limitations

   Bag-of-words treats each word separately, ignoring order and context.

2. Step 2: Analyze sentence complexity

   Sentence has negation "don't" which flips sentiment. Without context, model may misinterpret sentiment.

3. Step 3: Identify why classical methods fail

   Because they ignore word order and negation, they fail to capture true sentiment.

4. Final Answer:

   They treat words independently and miss negation and word order. -> Option B

5. Quick Check:

   Miss negation and order = D [OK]

Hint: Negation needs context; classical methods miss it [OK]

Common Mistakes:

• Thinking classical methods need GPUs
• Believing classical methods can't tokenize contractions
• Confusing overfitting with context loss