[Solved] Given the following Python code snippet using pre-trained GloVe embeddings, what will be the... Given the following Python code snippet using pre-trained GloVe embeddings, what will be the output? | NLP

NLP - Word Embeddings

Given the following Python code snippet using pre-trained GloVe embeddings, what will be the output?

from gensim.models import KeyedVectors

glove = KeyedVectors.load_word2vec_format('glove.6B.50d.txt', binary=False)
result = glove.similarity('king', 'queen')
print(round(result, 2))

A0.00

B0.78

C1.00

D-0.50

Step-by-Step Solution

Solution:

Step 1: Understand similarity method
The similarity method returns a cosine similarity score between two word vectors, usually between 0 and 1 for related words.
Step 2: Interpret expected similarity for 'king' and 'queen'
These words are closely related, so the similarity is high but less than 1, typically around 0.78.
Final Answer:
0.78 -> Option B
Quick Check:
Similarity('king','queen') ≈ 0.78 [OK]

Quick Trick: Related words have similarity close to but less than 1 [OK]

Common Mistakes:

MISTAKES

Assuming similarity is always 1 for related words
Confusing similarity with distance
Expecting negative similarity for related words

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More NLP Quizzes

Given the following Python code snippet using pre-trained GloVe embeddings, what will be the output?

Step 1: Understand similarity method

Step 2: Interpret expected similarity for 'king' and 'queen'

Final Answer:

Quick Check:

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