Word embeddings address the problem of synonymy by representing words in a multidimensional vector space, where similar words are located closer to each other. Synonymy refers to the phenomenon where multiple words have similar meanings. Traditional approaches, such as one-hot encoding, treat each word as an independent symbol and do not capture any semantic relationships between words. However, word embeddings are designed to capture semantic similarities by learning representations in a continuous vector space.

  Using word embeddings, words with similar meanings are expected to have similar vector representations. This enables the model to capture the subtle differences and nuances in meaning between words. These representations are learned from large amounts of text data through unsupervised learning algorithms such as Word2Vec, GloVe, or FastText.

  For example, consider the words "car" and "automobile," which are synonyms. In a well-trained word embedding model, these words are likely to have similar vector representations, meaning they will be located close to each other in the vector space. This proximity allows the model to identify the synonymy relationship between the two words and generalize their meanings. When these representations are used in downstream tasks like sentiment analysis or machine translation, the model can effectively leverage the semantic similarities between words to improve performance.

  Furthermore, word embeddings also capture other types of word relationships, such as antonyms, hyponyms, and meronyms. By analyzing the geometric relationships between vectors, it is possible to infer such relationships. For instance, by examining vector differences, one can identify that the relationship between "king" and "man" is similar to "queen" and "woman", representing gender-based relationships.

  In summary, word embeddings address the problem of synonymy by leveraging vector representations in a continuous space, where similar words are located closer to each other. This enables models to capture semantic relationships between words and generalize their meanings, resulting in improved performance in various natural language processing tasks.