An interactive dictionary needs vector features for words so the app can show words used in similar contexts. Which approach best provides those features for a nearest-neighbors model?

Downloading pre-trained word embedding vectors (like Word2Vec, GloVe, or FastText) is the most effective approach. These embeddings capture semantic relationships and contextual similarities between words by learning from vast amounts of text data, making them ideal for nearest-neighbors models in an interactive dictionary. One-hot encoding creates sparse, high-dimensional vectors that don't capture semantic similarity, making them unsuitable for finding words in similar contexts. Generating synonym lists manually is labor-intensive, subjective, and doesn't provide dense vector representations needed for similarity calculations. Edit distance measures character-level similarity, not semantic or contextual similarity, and doesn't produce suitable vectors for this purpose.