How does fastText differ from Word2vec in creating word embeddings?

fastText differs from Word2vec primarily in how it represents words when creating embeddings, specifically through the use of subword information. By utilizing individual letters and n-grams, fastText captures the morphology of words, allowing it to create more accurate embeddings for rare or unseen words. This is particularly beneficial in languages with rich morphology or when dealing with domain-specific vocabulary.

In contrast, Word2vec generates a fixed-length embedding for entire words without considering their internal structures or subword compositions. This can lead to limitations when dealing with out-of-vocabulary words, as Word2vec does not adapt to variations or morphological changes effectively.

Regarding the other options, fastText does support n-grams, thus expanding its capability to understand the context better by considering combinations of letters. Word2vec typically requires a well-sized dataset, but fastText can perform better with smaller datasets due to its ability to leverage subword information. The emphasis on individual letters in fastText’s methodology clearly distinguishes its approach from Word2vec.