Yes, word embeddings can be used for languages other than English. Word embeddings are essentially vector representations of words that capture semantic and syntactic information. These vectors are generated by training models on large corpora of text, such as news articles or books. As long as there is a sufficient amount of text available in the target language, word embeddings can be created.

  One popular method for creating word embeddings is the Word2Vec algorithm, which has been applied to many languages. By training a Word2Vec model on a large corpus of text in a specific language, it is possible to generate word embeddings that capture the relationships between words in that language.

  However, it is important to note that the quality and performance of word embeddings in different languages can vary. This is because the availability and quality of training data can differ across languages. Some languages may have limited text resources or may have different syntactic and semantic structures that require different modeling approaches.

  To work with languages other than English, it is essential to have access to a sizable and representative corpus of text in that language. Additionally, it may be necessary to preprocess the text data to handle specific linguistic features unique to the target language.