Embeddings for words and sentences are created through various techniques that transform text into numerical vectors, allowing computers to process and understand language more effectively. The fundamental idea is to represent words and sentences in a lower-dimensional space while preserving their semantic meanings. This is often done using methods such as Word2Vec, GloVe, or more complex models like BERT and its derivatives. Each word is assigned a vector based on its context in a large corpus of text, capturing relationships among words based on their usage.
For instance, in the Word2Vec approach, a neural network is trained on a large text dataset to predict a word given its surrounding words (the context), or vice versa. This model learns to position words that have similar meanings close together in the vector space. For example, the words "king" and "queen" may have vectors that are close to each other, as they share similar contextual usages. Similarly, sentence embeddings can be created by averaging the embeddings of individual words or by using models like Sentence-BERT that specifically optimize for sentence-level understanding.
In practice, once these embeddings are created, they can be used for various tasks, such as sentiment analysis or text classification. Developers can leverage pre-trained models which have already created embeddings for vast vocabularies, eliminating the need to train from scratch. They can then input their specific text data into these models to obtain embeddings. This step provides a compact representation of the text, making it easier and faster for machines to perform downstream tasks while retaining the contextual nuances of the original language.