Description: Word embedding is a technique used in natural language processing (NLP) that allows words to be represented in a continuous vector space. This representation captures the semantic meanings of words, facilitating their analysis and manipulation by artificial intelligence algorithms. Through embedding, words with similar meanings are placed close to each other in the vector space, enabling language models to understand relationships and contexts more effectively. This technique is based on the premise that the meaning of a word can be inferred from its context, resulting in vectors that reflect not only the identity of the word but also its associations and uses in different contexts. Word embedding has revolutionized the field of NLP, enabling significant advances in tasks such as machine translation, sentiment analysis, and text generation, among others. Its ability to transform words into numerical data has facilitated the integration of deep learning techniques in language processing, opening new possibilities for machines to understand and generate text.
History: The word embedding technique began to take shape in the early 2000s but gained prominence with the introduction of models like Word2Vec by Google in 2013. This model, developed by a team led by Tomas Mikolov, allowed computers to learn vector representations of words from large text corpora. Subsequently, other approaches like GloVe (Global Vectors for Word Representation) and FastText also contributed to the evolution of this technique, improving the quality and efficiency of embeddings.
Uses: Word embeddings are used in various natural language processing applications, such as machine translation, where they help map words from one language to another; in sentiment analysis, where they allow for the identification of emotions in texts; and in recommendation systems, where they are used to understand user preferences based on their interactions with content. They are also fundamental in the creation of chatbots and virtual assistants, enhancing their ability to understand and respond to user queries.
Examples: An example of word embedding is the Word2Vec model, which has been widely used in NLP applications. Another example is GloVe, which is used to represent words in a vector space based on word co-occurrence in large text corpora. FastText, which considers subwords, is also a relevant example, especially useful for handling rare or unknown words.
