Yes, word embeddings do consider word order to some extent. Word order is an important aspect of language semantics, and word embeddings aim to capture the meaning of words based on their context in a text.

  Word embeddings are generated using techniques like Word2Vec, GloVe, or FastText, which train models on large amounts of text data to learn word representations. These models take into account the surrounding words in the context window when learning the word embeddings.

  For example, in the Word2Vec model, a word is represented as a vector based on its co-occurrence with other words in the training data. The context window around each word determines which neighboring words are considered. By considering the order of the words in this window, the model can capture some information about the syntactic structure and the relationships between words.

  However, it is important to note that word embeddings are trained on fixed-size context windows, typically ranging from a few words to a few sentences. Longer-range dependencies or word order patterns that extend beyond the context window may not be fully captured by the embeddings.

  In summary, while word embeddings do consider word order to a certain extent, they are limited by the fixed-size context window used during training. Thus, they may not capture all nuances of word order in a text.