Word embeddings with gensim for semantic similarity tasks

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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))

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.

Dr. Elena Vasquez

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Word embeddings with gensim for semantic similarity tasks

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More from Dr. Elena Vasquez