Latent Dirichlet Allocation (LDA) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LDA model from scikit-learn.

NLP

from sklearn.decomposition import [1]

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

AKMeans

BPCA

CLatentDirichletAllocation

DTruncatedSVD

Attempts:

3 left

💡 Hint

Common Mistakes

Importing PCA or KMeans instead of LatentDirichletAllocation.

Misspelling the class name.

The correct import for Latent Dirichlet Allocation in scikit-learn is LatentDirichletAllocation.

2fill in blank

medium

Complete the code to create an LDA model with 5 topics.

NLP

lda = LatentDirichletAllocation(n_components=[1], random_state=42)

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

A5

B3

C10

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a different number of topics than requested.

Confusing n_components with other parameters.

Setting n_components=5 creates an LDA model with 5 topics.

3fill in blank

hard

Fix the error in the code to fit the LDA model on the document-term matrix named 'dtm'.

NLP

lda.fit([1])

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

Adocuments

Bdtm

Ctopics

Dlabels

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw documents or labels instead of the document-term matrix.

Using a variable that does not exist.

The LDA model fits on the document-term matrix, which is named dtm here.

4fill in blank

hard

Fill both blanks to get the topic distribution for the first document.

NLP

topic_distribution = lda.[1]([2])[0]

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

Atransform

Bdtm

Cfit_transform

Ddocuments

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform after fitting the model.

Passing raw documents instead of the document-term matrix.

Use transform on the document-term matrix dtm to get topic distributions. Index 0 gets the first document's distribution.

5fill in blank

hard

Fill all three blanks to create a dictionary of top words per topic from the LDA components.

NLP

top_words = {i: [feature_names[j] for j in lda.components_[i].argsort()[-[1]:][::-1]] for i in range([2])}
print(top_words)

# feature_names = vectorizer.get_feature_names_out()
# lda = LatentDirichletAllocation(n_components=[3])

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

A10

B5

Clda.n_components

Dlda.components_.shape[0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong number of top words like 10 instead of 5.

Confusing the number of topics with the number of words.

Using a fixed number instead of dynamic attributes.

We get the top 5 words per topic, so 10 is too many. The number of topics is the number of rows in lda.components_, which is lda.components_.shape[0]. The n_components parameter is the number of topics set in the model.

Practice

1. What is the main purpose of Latent Dirichlet Allocation (LDA) in natural language processing?

easy

A. To generate new sentences based on input text

B. To translate text from one language to another

C. To count the number of words in a document

D. To find hidden topics by grouping words that appear together in documents

Solution

1. Step 1: Understand LDA's function

   LDA is a method used to discover hidden topics in a collection of documents by grouping words that often appear together.

2. Step 2: Compare options with LDA's purpose

   Only To find hidden topics by grouping words that appear together in documents describes this process correctly. Other options describe different NLP tasks.

3. Final Answer:

   To find hidden topics by grouping words that appear together in documents -> Option D

4. Quick Check:

   LDA purpose = find hidden topics [OK]

Hint: LDA groups words to reveal hidden topics in texts [OK]

Common Mistakes:

• Confusing LDA with translation models
• Thinking LDA counts words only
• Assuming LDA generates new text

2. Which of the following is the correct way to initialize an LDA model using Python's gensim library?

easy

A. Lda(corpus=corpus, topics=5, dictionary=dictionary)

B. LdaModel(corpus=corpus, num_topics=5, id2word=dictionary)

C. LdaModel(corpus=corpus, topics=5, id2word=dictionary)

D. LdaModel(corpus=corpus, num_topics=5, dictionary=dictionary)

Solution

1. Step 1: Recall gensim LDA syntax

   The correct gensim LDA model initialization uses LdaModel with parameters corpus, num_topics, and id2word.

2. Step 2: Check each option

   LdaModel(corpus=corpus, num_topics=5, id2word=dictionary) matches the correct syntax exactly. Options A, C, and D have incorrect parameter names or missing required arguments.

3. Final Answer:

   LdaModel(corpus=corpus, num_topics=5, id2word=dictionary) -> Option B

4. Quick Check:

   gensim LDA init = LdaModel with num_topics [OK]

Hint: Use LdaModel with num_topics and id2word parameters [OK]

Common Mistakes:

• Using wrong parameter names like 'topics' instead of 'num_topics'
• Confusing dictionary parameter name
• Using Lda instead of LdaModel

3. Given the following code snippet using gensim LDA, what will be the output of print(ldamodel.print_topics(num_topics=2))?

from gensim.models.ldamodel import LdaModel
corpus = [[(0, 1), (1, 2)], [(0, 1), (2, 1)]]
dictionary = {0: 'apple', 1: 'banana', 2: 'cherry'}
ldamodel = LdaModel(corpus=corpus, num_topics=2, id2word=dictionary, random_state=42)
print(ldamodel.print_topics(num_topics=2))

medium

A. A list of tuples showing topics with words and their weights

B. [ (0, '0.6*banana + 0.4*apple'), (1, '0.7*cherry + 0.3*banana') ]

C. [ (0, '0.5*apple + 0.5*banana'), (1, '0.5*apple + 0.5*cherry') ]

D. SyntaxError due to incorrect dictionary format

Solution

1. Step 1: Understand print_topics output

   The print_topics method returns a list of tuples, each tuple contains a topic number and a string showing words with their weights.

2. Step 2: Analyze the code snippet

   The dictionary is a simple mapping, and the LDA model will output topics with word probabilities. The exact weights vary due to random initialization, so the output is a list of tuples with words and weights, not fixed numbers.

3. Final Answer:

   A list of tuples showing topics with words and their weights -> Option A

4. Quick Check:

   print_topics output = list of topic-word weight tuples [OK]

Hint: print_topics returns topic-word weights as tuples, not fixed values [OK]

Common Mistakes:

• Expecting exact numeric weights
• Confusing dictionary format causing errors
• Thinking output is a simple list of words only

4. You run an LDA model but get an error: AttributeError: 'dict' object has no attribute 'token2id'. What is the likely cause?

medium

A. Setting num_topics to zero

B. Using an empty corpus for training

C. Passing a Python dict instead of a gensim Dictionary object as id2word

D. Not installing gensim library

Solution

1. Step 1: Understand the error message

   The error says a 'dict' object lacks 'token2id', which is a property of gensim's Dictionary class, not a plain Python dict.

2. Step 2: Identify cause in LDA parameters

   Passing a plain dict as id2word instead of a gensim Dictionary causes this error because LDA expects a Dictionary object with token2id attribute.

3. Final Answer:

   Passing a Python dict instead of a gensim Dictionary object as id2word -> Option C

4. Quick Check:

   id2word must be gensim Dictionary, not plain dict [OK]

Hint: id2word must be gensim Dictionary, not plain dict [OK]

Common Mistakes:

• Passing plain dict instead of gensim Dictionary
• Ignoring error details about missing attributes
• Confusing corpus issues with dictionary errors

5. You want to use LDA to find 3 topics in a large collection of news articles. After training, you notice one topic has very similar words to another topic. What is a good way to improve topic separation?

hard

A. Remove stopwords and rare words before training

B. Reduce the number of topics to 1

C. Use the same model but increase training iterations

D. Increase the number of topics and retrain the model

Solution

1. Step 1: Understand why topics overlap

   Overlapping topics often happen because common words or noise confuse the model, making topics less distinct.

2. Step 2: Improve data quality before training

   Removing stopwords (common words) and rare words helps the model focus on meaningful words, improving topic separation.

3. Step 3: Evaluate other options

   Increasing topics may worsen overlap; reducing topics to 1 loses topic diversity; more iterations alone won't fix noisy data.

4. Final Answer:

   Remove stopwords and rare words before training -> Option A

5. Quick Check:

   Clean data improves topic separation [OK]

Hint: Clean data by removing stopwords to get clearer topics [OK]

Common Mistakes:

• Increasing topics without cleaning data
• Reducing topics too much losing detail
• Ignoring data preprocessing importance