Description: Token embedding is a fundamental technique in the field of Deep Learning, especially in natural language processing (NLP). It involves representing words or tokens in a continuous vector space, where each token is associated with a vector of real numbers. This representation allows capturing semantic and syntactic relationships between words, thus facilitating the understanding of the context in which they are used. Unlike discrete representations, such as using indices or one-hot encoding, token embeddings enable words with similar meanings to have vector representations that are close in space. This is crucial for tasks such as machine translation, sentiment analysis, and text generation, where understanding context and relationships between words is essential. Embeddings can be learned in a supervised or unsupervised manner and have become a standard tool in Deep Learning models, such as Word2Vec, GloVe, and more recently, in Transformer architectures like BERT and GPT. The ability of embeddings to generalize and capture nuances of human language has revolutionized the field of NLP, enabling significant advancements in the quality of AI-based applications.
History: The token embedding technique began to gain popularity in the early 2010s with the development of models like Word2Vec, created by a team of researchers at Google led by Tomas Mikolov in 2013. This model introduced the idea of learning vector representations of words from large text corpora, effectively capturing semantic relationships. Subsequently, GloVe (Global Vectors for Word Representation) was developed by researchers at Stanford in 2014, offering an alternative approach based on the word co-occurrence matrix. With the advancement of Deep Learning architectures, especially Transformer models like BERT and GPT, token embeddings have evolved to include dynamic contexts, leading to significant improvements in NLP tasks.
Uses: Token embeddings are primarily used in natural language processing for various applications. Some of their most notable uses include machine translation, where they help map words from one language to another while preserving meaning; sentiment analysis, where they enable the identification of emotions in texts; and text generation, where they facilitate the creation of coherent and relevant content. Additionally, they are used in recommendation systems, semantic search, and chatbots, enhancing the interaction between humans and machines.
Examples: A practical example of token embedding is the use of Word2Vec in a movie recommendation system, where movie descriptions are converted into vectors that allow finding similar titles based on semantic similarity. Another case is the use of BERT in a sentiment analysis model, where contextual embeddings help determine the polarity of opinions expressed on social media.
