Document-term matrix in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Document-Term Matrix Creation

What is the output of the following code that creates a document-term matrix from two simple documents?

NLP

from sklearn.feature_extraction.text import CountVectorizer
corpus = ['apple orange apple', 'orange banana orange']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(X.toarray())

A

[[1 2 0]
 [0 1 2]]

B

[[1 1 1]
 [1 1 1]]

C

[[2 0 1]
 [0 1 2]]

D

[[2 1 0]
 [0 2 1]]

💡 Hint

CountVectorizer counts how many times each word appears in each document.

The vectorizer finds three unique words: 'apple', 'banana', and 'orange'. The first document has 'apple' twice and 'orange' once, so its vector is [2, 0, 1]. The second document has 'orange' twice and 'banana' once, so its vector is [0, 1, 2]. However, the order of words is alphabetical: 'apple', 'banana', 'orange'. So the matrix is [[2, 0, 1], [0, 1, 2]]. The printed output matches option C.

🧠 Conceptual

intermediate

Understanding Document-Term Matrix Dimensions

If you create a document-term matrix from 5 documents containing a total of 100 unique words, what will be the shape (rows, columns) of the matrix?

A 100 rows and 5 columns

B 100 rows and 100 columns

C 5 rows and 5 columns

D 5 rows and 100 columns

💡 Hint

Rows represent documents, columns represent unique words.

A document-term matrix has one row per document and one column per unique word found in all documents. So with 5 documents and 100 unique words, the matrix shape is (5, 100).

❓ Metrics

advanced

Choosing the Right Metric for Document-Term Matrix Similarity

Which metric is most appropriate to measure similarity between two document vectors from a document-term matrix when the goal is to find documents with similar topics regardless of length?

A Cosine similarity

B Euclidean distance

C Manhattan distance

D Jaccard index

💡 Hint

Consider a metric that ignores vector length and focuses on direction.

Cosine similarity measures the angle between two vectors, ignoring their length. This is ideal for document vectors where length varies but topic similarity depends on word distribution.

🔧 Debug

advanced

Identifying the Error in Document-Term Matrix Code

What error will the following code raise? from sklearn.feature_extraction.text import CountVectorizer corpus = ['cat dog', 'dog mouse'] vectorizer = CountVectorizer() X = vectorizer.fit_transform(corpus) print(X[0, 1])

A No error, prints the count of the second word in the first document

B TypeError: 'csr_matrix' object is not subscriptable

C AttributeError: 'CountVectorizer' object has no attribute 'fit_transform'

D IndexError: index out of range

💡 Hint

csr_matrix supports indexing with X[i, j].

No error is raised. X is a csr_matrix, which supports direct indexing like X[0, 1]. Here, the vocabulary is ['cat', 'dog', 'mouse'] (alphabetical order), so X[0, 1] is the count of 'dog' in the first document, which is 1.

❓ Model Choice

expert

Best Model to Use with Document-Term Matrix for Text Classification

Given a document-term matrix representing text data, which machine learning model is generally most suitable for classifying documents into categories when the data is high-dimensional and sparse?

A K-Nearest Neighbors (KNN)

B Support Vector Machine (SVM) with linear kernel

C Decision Tree

D Naive Bayes

💡 Hint

Consider models that handle high-dimensional sparse data well and avoid overfitting.

SVM with a linear kernel works well on high-dimensional sparse data like document-term matrices. It finds a hyperplane that separates classes effectively and handles sparsity better than KNN or Decision Trees. Naive Bayes is also common but SVM often performs better in complex text classification.

Practice

1. What does a document-term matrix represent in natural language processing?

easy

A. The length of each document

B. The order of words in a sentence

C. The meaning of each word

D. Counts of words in each document

Solution

1. Step 1: Understand the purpose of a document-term matrix

   A document-term matrix counts how many times each word appears in each document.

2. Step 2: Compare options with this definition

   Only Counts of words in each document correctly describes this counting process.

3. Final Answer:

   Counts of words in each document -> Option D

4. Quick Check:

   Document-term matrix = word counts [OK]

Hint: Remember: matrix counts words per document [OK]

Common Mistakes:

• Confusing word order with counts
• Thinking it shows word meanings
• Assuming it measures document length

2. Which Python library provides the CountVectorizer class to create a document-term matrix?

easy

A. numpy

B. pandas

C. scikit-learn

D. matplotlib

Solution

1. Step 1: Recall the library for text feature extraction

   CountVectorizer is part of scikit-learn, a popular machine learning library.

2. Step 2: Verify other options

   numpy is for arrays, pandas for data frames, matplotlib for plotting, so they don't provide CountVectorizer.

3. Final Answer:

   scikit-learn -> Option C

4. Quick Check:

   CountVectorizer = scikit-learn [OK]

Hint: CountVectorizer is from scikit-learn, not numpy [OK]

Common Mistakes:

• Choosing numpy because it handles arrays
• Confusing pandas with text vectorization
• Selecting matplotlib for visualization

3. What is the output of this Python code snippet?

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

medium

A. [[1 1]
[1 2]]

B. [[1 1]
[2 1]]

C. [[2 1]
[1 2]]

D. [[1 2]
[1 1]]

Solution

1. Step 1: Identify the vocabulary and word counts

   The texts are 'cat dog' and 'dog dog cat'. Vocabulary sorted alphabetically is ['cat', 'dog']. First document has 1 'cat' and 1 'dog'. Second document has 1 'cat' and 2 'dog's.

2. Step 2: Form the document-term matrix

   Matrix rows correspond to documents, columns to words: [[1,1],[1,2]].

3. Final Answer:

   [[1 1] [1 2]] -> Option A

4. Quick Check:

   Word counts match matrix [OK]

Hint: Count words per document in alphabetical order [OK]

Common Mistakes:

• Mixing order of words in vocabulary
• Counting wrong number of word occurrences
• Confusing rows and columns

4. Identify the error in this code that tries to create a document-term matrix:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['apple orange', 'orange apple apple']
vectorizer = CountVectorizer()
X = vectorizer.transform(texts)
print(X.toarray())

medium

A. toarray() is not a method of X

B. Missing fit() before transform()

C. texts should be a single string, not a list

D. CountVectorizer() should be CountVector()

Solution

1. Step 1: Understand CountVectorizer usage

   CountVectorizer requires calling fit() or fit_transform() before transform() to learn vocabulary.

2. Step 2: Check the code sequence

   The code calls transform() directly without fit(), causing an error.

3. Final Answer:

   Missing fit() before transform() -> Option B

4. Quick Check:

   fit() needed before transform() [OK]

Hint: Always fit before transform with CountVectorizer [OK]

Common Mistakes:

• Skipping fit() step
• Using wrong class name
• Passing wrong data type to vectorizer

5. You have three documents: ['sun moon', 'moon moon sun', 'star sun moon']. Using CountVectorizer, what is the shape of the document-term matrix and which word has the highest total count across all documents?

hard

A. Shape (3, 3), 'moon' has highest count

B. Shape (3, 4), 'sun' has highest count

C. Shape (3, 3), 'sun' has highest count

D. Shape (3, 4), 'moon' has highest count

Solution

1. Step 1: Identify unique words and matrix shape

   Unique words are 'sun', 'moon', 'star' -> 3 words. There are 3 documents, so shape is (3, 3).

2. Step 2: Count total occurrences of each word

   'sun': appears 1 + 1 + 1 = 3 times 'moon': appears 1 + 2 + 1 = 4 times 'star': appears 0 + 0 + 1 = 1 time Highest count is 'moon' with 4.

3. Final Answer:

   Shape (3, 3), 'moon' has highest count -> Option A

4. Quick Check:

   3 docs x 3 words, moon count highest [OK]

Hint: Count unique words for shape, sum counts for highest word [OK]

Common Mistakes:

• Counting duplicate words as unique
• Mixing up shape dimensions
• Incorrectly summing word counts