Yes, word embeddings do consider word morphology to some extent. Word embeddings are mathematical representations of words in a vector space, where words with similar meanings are closer to each other in this space. The process of generating word embeddings takes into account the context in which words appear.

  One approach to generating word embeddings is through neural networks, specifically models like word2vec and GloVe. These models are typically trained on large amounts of text data and learn to represent words based on their co-occurrence patterns. By analyzing the context in which words appear, these models are able to capture some aspects of word morphology.

  For example, consider the words "run," "running," and "ran." Even though these words have different forms, they share a common morphological root. A well-trained word embedding model would learn to represent these words in a way that captures their similarity.

  Additionally, there are techniques that explicitly aim to incorporate word morphology into word embeddings. For example, morphological transformations such as stemming or lemmatization can be applied to words before training the word embeddings. These techniques normalize words to their base or root form, which can help capture morphological relationships between words.

  In summary, while word embeddings do not explicitly focus on word morphology, they do take it into account to some extent. The context in which words appear during training allows the models to capture certain morphological relationships between words. Additionally, preprocessing techniques like stemming or lemmatization can be applied to further enhance the representation of word morphology in word embeddings.