Document-term matrix in NLP - Model Pipeline Trace

Model Pipeline - Document-term matrix

A document-term matrix (DTM) is a way to turn text documents into numbers. It shows how often each word appears in each document. This helps computers understand and learn from text.

Data Flow - 5 Stages

1Raw text documents

5 documents x variable length→Collect raw text data→5 documents x variable length

["I love apples", "Apples are tasty", "I eat apples daily", "Tasty apples are good", "Love to eat fruits"]

↓

2Text cleaning

5 documents x variable length→Lowercase, remove punctuation, and extra spaces→5 documents x cleaned text

["i love apples", "apples are tasty", "i eat apples daily", "tasty apples are good", "love to eat fruits"]

↓

3Tokenization

5 documents x cleaned text→Split text into words (tokens)→5 documents x list of tokens

[["i", "love", "apples"], ["apples", "are", "tasty"], ["i", "eat", "apples", "daily"], ["tasty", "apples", "are", "good"], ["love", "to", "eat", "fruits"]]

↓

4Build vocabulary

5 documents x list of tokens→Find unique words across all documents→Vocabulary size: 10 words

["i", "love", "apples", "are", "tasty", "eat", "daily", "good", "to", "fruits"]

↓

5Create document-term matrix

5 documents x list of tokens→Count how many times each word appears in each document→5 documents x 10 words

[[1,1,1,0,0,0,0,0,0,0], [0,0,1,1,1,0,0,0,0,0], [1,0,1,0,0,1,1,0,0,0], [0,0,1,1,1,0,0,1,0,0], [0,1,0,0,0,1,0,0,1,1]]

Training Trace - Epoch by Epoch

Loss
0.9 |*
0.8 |** 
0.7 |***  
0.6 |****  
0.5 |*****   
0.4 |******   
0.3 |*******    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.50	Initial training with sparse document-term matrix input
2	0.65	0.65	Model learns word patterns better
3	0.50	0.75	Loss decreases and accuracy improves steadily
4	0.40	0.82	Model converging with good performance
5	0.35	0.85	Final epoch shows stable improvement

Prediction Trace - 3 Layers

Layer 1: Input document

Layer 2: Vectorization using document-term matrix

Layer 3: Model prediction

Model Quiz - 3 Questions

What does each row in a document-term matrix represent?

A A document with counts of each word

B A word with counts of each document

C A list of unique words

D A cleaned text sentence

Key Insight

A document-term matrix transforms text into numbers by counting word occurrences. This numeric form allows machine learning models to find patterns in text and improve predictions as training progresses.

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