GloVe embeddings in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What does GloVe embeddings capture?

GloVe embeddings are used to represent words as vectors. What key information do these vectors capture?

A The global co-occurrence statistics of words in a corpus

B The frequency of each word in the corpus only

C Only the syntactic structure of sentences

D Random numeric values assigned to words

💡 Hint

Think about how GloVe uses word pairs appearing together in the whole text.

GloVe embeddings are trained by analyzing how often words appear together across the entire text corpus, capturing semantic relationships.

❓ Predict Output

intermediate

Output of GloVe vector lookup

Given a loaded GloVe embedding dictionary glove_vectors where keys are words and values are vectors, what is the output of the code below?

NLP

word = 'king'
vector = glove_vectors.get(word)
print(len(vector))

A 100

B 300

C 50

D KeyError

💡 Hint

Common GloVe embeddings come in sizes like 50, 100, 200, or 300 dimensions.

The standard GloVe embeddings often have 300 dimensions, so the vector length for 'king' is 300.

❓ Model Choice

advanced

Choosing GloVe for a sentiment analysis task

You want to build a sentiment analysis model on movie reviews. Which reason best justifies choosing pre-trained GloVe embeddings over training embeddings from scratch?

A GloVe embeddings only capture syntax, which is irrelevant for sentiment.

B GloVe embeddings are always smaller in size, making the model faster regardless of data.

C GloVe embeddings are trained on large corpora and capture rich semantic relations, improving model performance with less data.

D Training embeddings from scratch always leads to better results than using GloVe.

💡 Hint

Think about the benefits of using embeddings trained on large text collections.

Pre-trained GloVe embeddings have learned word meanings from huge datasets, so they help models understand words better, especially when your own data is limited.

❓ Hyperparameter

advanced

Effect of embedding dimension size in GloVe

When training GloVe embeddings, what is the effect of increasing the embedding dimension size from 50 to 300?

A It decreases the model's ability to learn word meanings.

B It makes the embeddings sparse and less useful.

C It has no effect on the quality or size of embeddings.

D It increases the ability to capture complex word relationships but requires more memory and computation.

💡 Hint

Think about trade-offs between vector size and information captured.

Larger embedding dimensions allow capturing more detailed word relationships but increase memory use and computation time.

❓ Metrics

expert

Evaluating GloVe embeddings quality

You trained GloVe embeddings on a custom corpus. Which metric best helps evaluate if the embeddings capture meaningful semantic relationships?

A Cosine similarity between vectors of related word pairs

B Mean squared error of embedding vectors

C Number of unique words in the corpus

D Training loss of a separate classification model

💡 Hint

Think about how to measure if similar words have similar vectors.

Cosine similarity measures how close vectors are in direction, so high similarity for related words shows embeddings capture meaning well.