Subword embeddings represent parts of words (such as prefixes, suffixes, or character n-grams) rather than entire words. These embeddings are particularly useful for handling rare or unseen words by breaking them down into smaller, meaningful components.
For example, in subword models like FastText, the word "running" might be broken into subwords like "run," "ning," and "ing." This approach allows the model to generalize better, as similar words share common subwords, even if they were not seen during training.
Subword embeddings are especially valuable in languages with rich morphology or large vocabularies, as they help reduce the number of unknown words and improve performance on tasks like machine translation and text classification. By focusing on smaller components, subword embeddings capture more granular relationships within the text.