Yes, word embeddings are designed to capture context-dependent meaning to a certain extent. Word embeddings represent words as high-dimensional vectors in a continuous space, where similar words are represented by vectors that are close to each other. These embeddings are usually generated using neural network models trained on large text corpora.

  One of the advantages of word embeddings is that they can capture semantic relationships between words. Words that appear in similar contexts tend to have similar embeddings, which suggests that they share similar meanings. For example, in a trained word embedding model, the vectors for the words "dog" and "cat" are likely to be more similar to each other than to the word "car".

  Word embeddings can also capture context-dependent meaning by considering the local context in which words appear. The vectors of neighboring words in a sentence can influence the embedding of a target word. For instance, in the sentence "I saw a bat in the cave," the word "bat" can have different embeddings depending on whether it refers to a flying mammal or a sports equipment. The embeddings can reflect these contextual differences.

  However, it's important to note that word embeddings have their limitations when it comes to capturing context-dependent meaning. They rely on the assumption that words with similar meanings will occur in similar contexts, which is not always the case. Word embeddings may struggle to distinguish between homonyms or words with multiple meanings that have different contexts. Additionally, word embeddings generally cannot capture complex semantic relationships, such as negation or analogy, as effectively as humans can.

  To overcome these limitations, researchers have developed more advanced models such as contextualized word embeddings, such as BERT (Bidirectional Encoder Representations from Transformers). These models take into account the entire context of a word in a sentence, enabling them to capture more nuanced and context-dependent meanings.