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Word embeddings with gensim for semantic similarity tasks

Dr. Elena Vasquez Apr 2026

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from gensim.models import Word2Vec

sentences = [
    ['customer', 'refund', 'payment', 'issue'],
    ['login', 'authentication', 'password', 'reset'],
    ['delivery', 'shipment', 'tracking', 'delay'],
    ['refund', 'chargeback', 'billing', 'payment'],
]

model = Word2Vec(sentences=sentences, vector_size=100, window=5, min_count=1, workers=2, sg=1)
print(model.wv.most_similar('refund', topn=3))

1 file · python Explain with highlit

Dense embeddings help when lexical overlap is weak but semantic similarity matters. I use them for retrieval prototypes, clustering, and feature enrichment when transformer infrastructure is overkill. The main discipline is keeping training data clean and checking nearest neighbors for obvious failure modes.

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