LDA with Gensim in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of LDA Topic Distribution

Given the following code snippet using Gensim's LDA model, what is the output of the print statement?

NLP

import gensim
from gensim import corpora

texts = [['apple', 'banana', 'apple', 'fruit'], ['banana', 'fruit', 'fruit', 'apple'], ['car', 'engine', 'wheel'], ['engine', 'car', 'wheel', 'car']]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

lda = gensim.models.LdaModel(corpus, num_topics=2, id2word=dictionary, random_state=42, passes=10)

print(lda[corpus[0]])

A [(0, 0.05), (1, 0.95)]

B [(0, 0.5), (1, 0.5)]

C [(0, 0.95), (1, 0.05)]

D [(0, 1.0), (1, 0.0)]

💡 Hint

Think about how LDA assigns topic probabilities to documents based on word distributions.

The first document mostly contains words related to the first topic, so the model assigns a high probability to topic 0 and a low probability to topic 1.

❓ Model Choice

intermediate

Choosing Number of Topics in LDA

You want to model topics in a collection of news articles using Gensim's LDA. Which approach best helps decide the number of topics?

A Use domain knowledge to guess a reasonable number and validate with coherence scores.

B Set num_topics to a very high number and pick the one with the highest coherence score.

C Always set num_topics to 2 for simplicity.

D Use the number of documents as num_topics.

💡 Hint

Think about balancing model complexity and interpretability.

Choosing num_topics based on domain knowledge and validating with coherence scores helps find a meaningful and interpretable number of topics.

❓ Hyperparameter

advanced

Effect of passes Parameter in Gensim LDA

What is the effect of increasing the passes parameter when training an LDA model with Gensim?

A It increases the number of times the model iterates over the entire corpus, potentially improving convergence.

B It changes the number of topics the model will find.

C It controls the number of words in each topic.

D It sets the random seed for reproducibility.

💡 Hint

Think about how many times the model sees the data during training.

The passes parameter controls how many times the model goes through the whole corpus during training, which can help the model converge better.

❓ Metrics

advanced

Interpreting Coherence Score in LDA

You trained two LDA models with different numbers of topics. Model A has a coherence score of 0.42, and Model B has 0.58. What does this imply?

A Coherence scores do not relate to topic quality.

B Model A has better topic diversity than Model B.

C Model A is overfitting the data compared to Model B.

D Model B's topics are more semantically coherent and likely more interpretable.

💡 Hint

Higher coherence scores usually mean better topic quality.

Coherence measures how semantically related the top words in topics are. Higher scores indicate more meaningful topics.

🔧 Debug

expert

Identifying Error in LDA Corpus Preparation

What error will this code raise when running the LDA model training?

NLP

import gensim
from gensim import corpora

texts = [['dog', 'cat'], ['cat', 'mouse'], ['dog', 'mouse']]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# Incorrect corpus: passing list of words instead of bow vectors
lda = gensim.models.LdaModel(texts, num_topics=2, id2word=dictionary, passes=5)

A TypeError: unhashable type: 'list'

B AttributeError: 'list' object has no attribute 'get'

C No error, model trains successfully

D ValueError: corpus must be a list of bag-of-words vectors

💡 Hint

Check what type the LDA model expects as corpus input.

The LDA model expects a corpus of bag-of-words vectors, but the code passes raw token lists, causing an AttributeError when the model tries to access methods on the corpus elements.

Practice

1. What is the main purpose of using LDA (Latent Dirichlet Allocation) with Gensim in NLP?

easy

A. To find hidden topics in a collection of documents

B. To translate text from one language to another

C. To count the frequency of words in a document

D. To generate new sentences based on input text

Solution

1. Step 1: Understand LDA's goal

   LDA is a topic modeling technique used to discover hidden topics in text data.

2. Step 2: Match with Gensim usage

   Gensim's LDA implementation helps find these hidden topics from document collections.

3. Final Answer:

   To find hidden topics in a collection of documents -> Option A

4. Quick Check:

   LDA purpose = find hidden topics [OK]

Hint: LDA = discover hidden themes in text collections [OK]

Common Mistakes:

• Confusing LDA with translation or text generation
• Thinking LDA counts word frequency only
• Assuming LDA summarizes text instead of finding topics

2. Which of the following is the correct way to create a Gensim dictionary from tokenized documents stored in texts?

easy

A. dictionary = gensim.make_dictionary(texts)

B. dictionary = gensim.Dictionary(texts)

C. dictionary = gensim.corpora.Dictionary(texts)

D. dictionary = gensim.create_dictionary(texts)

Solution

1. Step 1: Recall Gensim dictionary creation syntax

   The correct method is gensim.corpora.Dictionary() which takes tokenized texts.

2. Step 2: Check options for exact match

   Only dictionary = gensim.corpora.Dictionary(texts) uses the full correct syntax with gensim.corpora.Dictionary.

3. Final Answer:

   dictionary = gensim.corpora.Dictionary(texts) -> Option C

4. Quick Check:

   Correct dictionary syntax = dictionary = gensim.corpora.Dictionary(texts) [OK]

Hint: Use gensim.corpora.Dictionary for token lists [OK]

Common Mistakes:

• Omitting 'corpora' module in gensim
• Using non-existent functions like make_dictionary
• Confusing dictionary creation with corpus creation

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

import gensim
from gensim import corpora
texts = [['apple', 'banana', 'apple'], ['banana', 'orange'], ['apple', 'orange', 'banana']]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
ldamodel = gensim.models.LdaModel(corpus, num_topics=2, id2word=dictionary, passes=10, random_state=42)
print(ldamodel.print_topics(num_topics=2))

medium

A. Empty list because no topics were found

B. [('0', '0.5*"apple" + 0.3*"banana" + 0.2*"orange"'), ('1', '0.6*"banana" + 0.4*"orange"')]

C. SyntaxError due to missing import of LdaModel

D. A list of tuples showing topic IDs and top words with weights

Solution

1. Step 1: Understand print_topics output

   print_topics returns a list of tuples with topic IDs and top words with weights as strings.

2. Step 2: Analyze code correctness

   Code imports gensim and corpora correctly, creates dictionary and corpus, trains LDA model, so output is topic list, not error or empty.

3. Final Answer:

   A list of tuples showing topic IDs and top words with weights -> Option D

4. Quick Check:

   print_topics output = topic list [OK]

Hint: print_topics returns topic-word lists, not exact strings [OK]

Common Mistakes:

• Expecting exact word weights as fixed numbers
• Assuming missing import causes error (gensim.models is imported)
• Thinking no topics found means empty list

4. You run the following code but get an error: AttributeError: 'LdaModel' object has no attribute 'show_topics'. What is the likely cause?

ldamodel = gensim.models.LdaModel(corpus, num_topics=3, id2word=dictionary)
print(ldamodel.show_topics())

medium

A. The dictionary was not created properly

B. Using an outdated Gensim version where show_topics is not available

C. The corpus variable is empty or None

D. Missing the 'passes' parameter in LdaModel initialization

Solution

1. Step 1: Identify error meaning

   AttributeError means the method show_topics does not exist on the LdaModel object.

2. Step 2: Check common causes

   Older Gensim versions did not have show_topics method; newer versions do. Missing passes or empty corpus cause different errors.

3. Final Answer:

   Using an outdated Gensim version where show_topics is not available -> Option B

4. Quick Check:

   AttributeError on show_topics = outdated Gensim [OK]

Hint: Check Gensim version if method not found error occurs [OK]

Common Mistakes:

• Assuming missing passes causes AttributeError
• Thinking empty corpus causes this error
• Blaming dictionary creation for method missing

5. You want to improve your LDA model's topic quality using Gensim. Which combination of actions is best?

1. Increase the number of passes during training
2. Remove very common words (stopwords) before training
3. Use a very large number of topics (e.g., 100) regardless of data size
4. Filter out words that appear in too few or too many documents

hard

A. Apply steps 1, 2, and 4 to improve model quality

B. Only increase passes (step 1) is enough for better topics

C. Use a very large number of topics (step 3) for best results

D. Remove stopwords (step 2) and increase topics (step 3) only

Solution

1. Step 1: Understand passes effect

   More passes let the model learn better from data, improving topic quality.

2. Step 2: Understand preprocessing impact

   Removing stopwords and filtering rare/common words reduces noise and improves topics.

3. Step 3: Avoid too many topics

   Using too many topics without enough data causes poor, fragmented topics.

4. Final Answer:

   Apply steps 1, 2, and 4 to improve model quality -> Option A

5. Quick Check:

   Good LDA = passes + clean data + filter words [OK]

Hint: More passes + clean data + filter words = better topics [OK]

Common Mistakes:

• Thinking more topics always improves quality
• Ignoring data cleaning steps
• Believing passes alone fix poor topics