Embeddings play a crucial role in text similarity tasks by representing words, sentences, or documents as vectors in a high-dimensional space. The key advantage of embeddings is that semantically similar texts are mapped to nearby points in this space, making it easy to compare them. For example, in tasks like document similarity, two documents that discuss similar topics will have embeddings that are close to each other.
To measure text similarity, various distance metrics, like cosine similarity or Euclidean distance, are used to calculate how close or far apart two embeddings are in the vector space. This makes embeddings particularly useful in applications like information retrieval, where you need to find documents or sentences that are most relevant to a given query. In sentiment analysis, embeddings can also help assess how similar a piece of text is to another in terms of its emotional tone or meaning.
By using embeddings, text similarity tasks become more efficient and accurate, as embeddings capture the underlying meanings of words or phrases. They enable systems to identify related concepts even when the exact words or phrases are not present, improving tasks such as paraphrase detection, plagiarism detection, and search engine relevance.