Yes, word embeddings do preserve syntactic relationships between words to a certain extent. Word embeddings are vector representations of words that capture their semantic and syntactic properties based on the context in which they appear in a given corpus of text.

  One of the key advantages of word embeddings is their ability to capture the context and meaning of words in a way that traditional one-hot encoded representations cannot. This means that words with similar syntactic relationships are likely to have similar vector representations in the embedding space.

  For example, consider a sentence like "The cat jumped over the wall." In a properly trained word embedding model, the vectors for the words "cat" and "jumped" are expected to be closer to each other compared to the vectors for "cat" and "wall." This is because the verb "jumped" represents a syntactic relationship involving the subject "cat" and the object "wall."

  Furthermore, word embeddings can also capture more complex syntactic relationships, such as verb tense or noun-verb agreement. For instance, the vectors for the words "run" and "running" are expected to be close to each other, reflecting their shared syntactic relationship.

  However, it is important to note that word embeddings are not perfect and may not preserve all syntactic relationships perfectly. They are trained on large corpora, and the quality of the embeddings depends on factors such as the size and representativeness of the training data, the algorithm used for training, and the specific context window size chosen.

  In summary, while word embeddings generally do a good job of preserving syntactic relationships between words, they are not flawless and may have limitations depending on the training process and the specific dataset used.