First NLP pipeline - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this tokenization code?

Given the following Python code using NLTK, what is the output of the tokens variable?

NLP

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
text = "Hello world! This is a test."
tokens = word_tokenize(text)
print(tokens)

A ['Hello world!', 'This is a test.']

B ['Hello', 'world', '!', 'This', 'is', 'a', 'test', '.']

C ['Hello', 'world!', 'This', 'is', 'a', 'test.']

D ['Hello', 'world', 'This', 'is', 'a', 'test']

💡 Hint

Think about how word_tokenize splits punctuation as separate tokens.

The word_tokenize function splits the text into words and punctuation marks separately, so punctuation like '!' and '.' become their own tokens.

❓ Model Choice

intermediate

Which model is best for sentiment analysis in an NLP pipeline?

You want to build a simple NLP pipeline to classify movie reviews as positive or negative. Which model is most suitable?

A A convolutional neural network for image classification

B A K-Means clustering model

C A linear regression model

D A pretrained BERT model fine-tuned on sentiment data

💡 Hint

Sentiment analysis is a text classification task; choose a model designed for text understanding.

BERT is a powerful language model that can be fine-tuned for sentiment classification tasks, unlike clustering or regression models or image CNNs.

❓ Hyperparameter

advanced

Which hyperparameter affects the number of words considered in a Bag-of-Words model?

In a Bag-of-Words NLP pipeline using CountVectorizer, which hyperparameter controls the maximum number of words (features) to keep?

A stop_words

B min_df

C max_features

D ngram_range

💡 Hint

This parameter limits the vocabulary size by frequency.

max_features sets the maximum number of features (words) to keep based on frequency, controlling vocabulary size.

❓ Metrics

advanced

Which metric is best to evaluate an imbalanced text classification model?

You trained an NLP model to detect spam emails, but spam emails are only 5% of your data. Which metric is best to evaluate your model?

A Precision and Recall

B Accuracy

C Mean Squared Error

D R-squared

💡 Hint

Accuracy can be misleading when classes are imbalanced.

Precision and recall give better insight on performance for imbalanced classes by measuring false positives and false negatives.

🔧 Debug

expert

Why does this NLP pipeline code raise a KeyError?

Consider this code snippet for text preprocessing:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['I love AI', 'AI is fun']
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)
print(vectorizer.vocabulary_['AI'])

Why does it raise a KeyError for 'AI'?

NLP

from sklearn.feature_extraction.text import CountVectorizer
texts = ['I love AI', 'AI is fun']
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)
print(vectorizer.vocabulary_['AI'])

A 'AI' is not in the vocabulary because CountVectorizer lowercases all tokens by default

B 'AI' is removed because it is considered a stop word in English

C The vocabulary_ attribute is not a dictionary, causing the error

D The fit_transform method was not called before accessing vocabulary_

💡 Hint

Check how CountVectorizer processes text tokens before building vocabulary.

CountVectorizer lowercases all tokens by default, so 'AI' becomes 'ai'. Accessing 'AI' (uppercase) causes KeyError.

Practice

1. What is the main purpose of an NLP pipeline in machine learning?

easy

A. To translate text into different languages automatically

B. To store large amounts of text data

C. To process text step-by-step for making predictions

D. To create images from text

Solution

1. Step 1: Understand the role of an NLP pipeline

   An NLP pipeline breaks down text processing into steps like cleaning, vectorizing, and modeling.

2. Step 2: Identify the goal of these steps

   The goal is to prepare text data so a model can make predictions, such as classifying or understanding text.

3. Final Answer:

   To process text step-by-step for making predictions -> Option C

4. Quick Check:

   NLP pipeline = step-by-step text processing for predictions [OK]

Hint: Remember: pipeline means step-by-step processing [OK]

Common Mistakes:

• Thinking pipeline stores data only
• Confusing pipeline with translation tools
• Assuming pipeline creates images

2. Which of the following is the correct way to import a text vectorizer from scikit-learn for an NLP pipeline?

easy

A. import CountVectorizer from sklearn.text

B. from sklearn.feature_extraction.text import CountVectorizer

C. from sklearn.vectorizer import TextCount

D. import text_vectorizer from sklearn.feature

Solution

1. Step 1: Recall the correct module for text vectorizers

   Scikit-learn provides CountVectorizer in the feature_extraction.text module.

2. Step 2: Check the import syntax

   The correct syntax is: from sklearn.feature_extraction.text import CountVectorizer.

3. Final Answer:

   from sklearn.feature_extraction.text import CountVectorizer -> Option B

4. Quick Check:

   Correct import = from sklearn.feature_extraction.text import CountVectorizer [OK]

Hint: Remember: CountVectorizer is in feature_extraction.text [OK]

Common Mistakes:

• Using wrong module names
• Incorrect import syntax
• Confusing class names

3. Given the following code snippet, what will be the output of print(X.toarray())?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['cat and dog', 'dog and mouse']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
print(X.toarray())

medium

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

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

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

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

Solution

1. Step 1: Identify the vocabulary from the texts

   The texts are 'cat and dog' and 'dog and mouse'. The unique words are: 'and', 'cat', 'dog', 'mouse'. CountVectorizer sorts them alphabetically: ['and', 'cat', 'dog', 'mouse'].

2. Step 2: Map each text to counts of these words

   First text: 'cat and dog' -> counts: and=1, cat=1, dog=1, mouse=0 -> [1 1 1 0]. Second text: 'dog and mouse' -> counts: and=1, cat=0, dog=1, mouse=1 -> [1 0 1 1].

3. Final Answer:

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

4. Quick Check:

   Vocabulary order and counts match [[1 1 1 0] [1 0 1 1]] [OK]

Hint: Remember: CountVectorizer sorts words alphabetically [OK]

Common Mistakes:

• Mixing word order in output
• Confusing counts of words
• Assuming different vocabulary order

4. You wrote this code but get an error: AttributeError: 'CountVectorizer' object has no attribute 'transform_text'. What is the likely fix?

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
vectorizer.transform_text(['hello world'])

medium

A. Replace transform_text with transform

B. Import CountVectorizer from a different module

C. Call fit before transform_text

D. Use fit_transform_text instead

Solution

1. Step 1: Identify the incorrect method name

   The error says 'CountVectorizer' has no method 'transform_text'. The correct method is 'transform'.

2. Step 2: Correct the method call

   Replace transform_text with transform to fix the error.

3. Final Answer:

   Replace transform_text with transform -> Option A

4. Quick Check:

   Correct method name is transform [OK]

Hint: Check method names carefully in docs [OK]

Common Mistakes:

• Using non-existent method names
• Not reading error messages
• Trying to call fit_transform_text which doesn't exist

5. You want to build a simple NLP pipeline that converts text to numbers and then trains a logistic regression model to classify text. Which sequence of steps is correct?

hard

A. Predict on new text -> Vectorize text -> Train logistic regression

B. Train logistic regression -> Vectorize text -> Predict on new text

C. Vectorize text -> Predict on new text -> Train logistic regression

D. Vectorize text -> Train logistic regression -> Predict on new text

Solution

1. Step 1: Understand the pipeline order

   First, text must be converted to numbers using vectorization before training a model.

2. Step 2: Follow logical flow

   After vectorizing, train the logistic regression model, then use it to predict on new vectorized text.

3. Final Answer:

   Vectorize text -> Train logistic regression -> Predict on new text -> Option D

4. Quick Check:

   Correct pipeline order = Vectorize text -> Train logistic regression -> Predict on new text [OK]

Hint: Always vectorize before training or predicting [OK]

Common Mistakes:

• Trying to train before vectorizing
• Predicting before training
• Skipping vectorization step