Latent Dirichlet Allocation (LDA) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What does the 'topic' represent in LDA?

In Latent Dirichlet Allocation, what does a 'topic' most accurately represent?

A A single word that best describes a document

B A fixed label assigned to each document

C A cluster of documents grouped by similarity

D A distribution over words showing which words are likely to appear together

💡 Hint

Think about how LDA models topics as probabilities over vocabulary.

In LDA, each topic is a probability distribution over words, indicating which words are likely to co-occur in that topic.

❓ Predict Output

intermediate

Output of LDA topic distribution for a document

Given the following Python code using sklearn's LDA, what is the shape of doc_topic_dist?

NLP

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["apple banana apple", "banana orange banana", "apple orange orange"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2, random_state=0)
lda.fit(dtm)
doc_topic_dist = lda.transform(dtm)
print(doc_topic_dist.shape)

A (3, 2)

B (2, 3)

C (3, 3)

D (2, 2)

💡 Hint

Check how many documents and topics are in the model.

The transform method returns the topic distribution for each document. There are 3 documents and 2 topics, so the shape is (3, 2).

❓ Model Choice

advanced

Choosing the number of topics in LDA

You want to model topics in a large collection of news articles using LDA. Which approach is best to decide the number of topics?

A Use domain knowledge and try multiple values, then select based on coherence or perplexity scores

B Always set the number of topics to 10 by default

C Set the number of topics equal to the number of documents

D Use the number of unique words as the number of topics

💡 Hint

Think about how to balance model complexity and interpretability.

Choosing the number of topics is often done by testing multiple values and evaluating metrics like coherence or perplexity, combined with domain knowledge.

❓ Metrics

advanced

Interpreting LDA perplexity score

After training an LDA model, you get a perplexity score of 1200 on your test set. What does a lower perplexity score indicate?

A The model is overfitting the training data

B The model predicts the test data better, indicating better generalization

C The model has fewer topics

D The model is ignoring rare words

💡 Hint

Perplexity measures how well the model predicts unseen data.

A lower perplexity means the model better predicts the test data, showing better generalization.

🔧 Debug

expert

Identifying error in LDA code snippet

What error will this code raise?

NLP

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["cat dog", "dog mouse", "cat mouse"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=3, random_state=0)
lda.fit_transform(dtm)
print(lda.components_.shape)

A TypeError: fit_transform() missing 1 required positional argument

B AttributeError: 'LatentDirichletAllocation' object has no attribute 'components_'

C (3, 3)

D ValueError: n_components must be less than or equal to number of features

💡 Hint

Check the shape of the document-term matrix and the number of topics.

The document-term matrix has 3 features (unique words). The model has 3 topics, so components_ shape is (3, 3). No error occurs.

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