Find Similar Words Using Embeddings in NLP: Simple Guide

To find similar words using embeddings in NLP, convert words into vector representations and measure similarity using metrics like cosine similarity. Words with vectors close in space are considered similar.

Syntax

Use a pre-trained embedding model to get word vectors, then compute similarity between vectors.

• embedding_model[word]: gets vector for a word.
• cosine_similarity(vec1, vec2): measures similarity between two vectors.
• Find words with highest similarity scores to the target word.

python

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Example: get vector for a word
word_vector = embedding_model['word']

# Compute similarity between two vectors
similarity = cosine_similarity([vec1], [vec2])[0][0]

# Find top similar words by comparing similarity scores

Example

This example uses the gensim library's pre-trained Word2Vec model to find words similar to 'king'. It shows how to load embeddings, query similar words, and print results.

python

import gensim.downloader as api

# Load pre-trained Word2Vec embeddings
embedding_model = api.load('word2vec-google-news-300')

# Find top 5 words similar to 'king'
similar_words = embedding_model.most_similar('king', topn=5)

# Print similar words and their similarity scores
for word, score in similar_words:
    print(f'{word}: {score:.3f}')

Output

queen: 0.783
prince: 0.755
monarch: 0.732
crown: 0.711
throne: 0.700

Common Pitfalls

• Using embeddings without normalization can give wrong similarity scores.
• Not handling out-of-vocabulary words causes errors.
• Confusing Euclidean distance with cosine similarity; cosine is preferred for word vectors.
• Using small or poorly trained embeddings leads to poor similarity results.

python

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Wrong: Using raw dot product instead of cosine similarity
similarity_wrong = np.dot(vec1, vec2)

# Right: Use cosine similarity for correct similarity measure
similarity_right = cosine_similarity([vec1], [vec2])[0][0]

Quick Reference

Tips for finding similar words using embeddings:

• Use pre-trained embeddings like Word2Vec, GloVe, or FastText.
• Compute similarity with cosine similarity for best results.
• Handle unknown words by checking if they exist in the embedding vocabulary.
• Normalize vectors if computing similarity manually.

Key Takeaways

Convert words to vectors using pre-trained embedding models to compare similarity.

Use cosine similarity to measure how close two word vectors are in meaning.

Handle words not in the embedding vocabulary to avoid errors.

Pre-trained embeddings like Word2Vec provide ready-to-use vectors for many words.

Avoid using raw dot product; always prefer cosine similarity for word vectors.