Yes, word embeddings can handle out-of-vocabulary (OOV) words to some extent. Word embeddings are vector representations of words that capture semantic and syntactic relationships between words based on their context in a given corpus. These embeddings are typically learned through unsupervised machine learning algorithms like Word2Vec or GloVe.

  When encountering an OOV word, embeddings can still be useful. The embeddings are generally learned on a large corpus of text and any word that occurs in that corpus will have a vector representation. Even if a specific word was not seen during the training process, its embedding can be approximated based on the similarity to known words.

  One way to handle OOV words is to use subword embeddings. These embeddings break down words into smaller units such as character n-grams or morphemes, and then learn embeddings for these subword units. By doing so, the model can handle OOV words by combining the embeddings of the subword units that make up the word.

  Another approach is to use pre-trained embeddings, such as those provided by Word2Vec or GloVe. These embeddings are trained on massive amounts of text, so they have representations for a wide range of words. Even if a word is not present in the training corpus, its embedding can still be obtained by choosing the embedding of a similar word or by using a statistical method.

  However, it is important to note that if the number of OOV words is too high or if the OOV words are very different from the words seen in the training corpus, the quality of the approximated embeddings may be lower. In such cases, additional techniques like fallback strategies or domain-specific embeddings may be necessary to handle OOV words effectively.