Description: Sentence embeddings are representations of sentences in a continuous vector space, where each sentence is translated into a vector of real numbers. This technique allows capturing the semantic meaning of sentences, facilitating text comparison and analysis. Unlike traditional representations, such as ‘bag of words’, which ignore the order and structure of words, sentence embeddings consider the context and relationships between words. This is achieved through language models that have been trained on large text corpora, learning to map similar sentences to nearby vectors in the vector space. This property of proximity in the vector space is fundamental for natural language processing (NLP) tasks, as it enables machines to understand and process human language more effectively. Sentence embeddings are particularly useful in applications that require a deep understanding of meaning, such as machine translation, semantic search, and text classification. In summary, sentence embeddings are a powerful tool in the field of natural language processing, providing an efficient and effective way to represent and analyze natural language.
History: Sentence embeddings emerged as an extension of word embeddings, which began to gain popularity with the introduction of Word2Vec by Google in 2013. As research in natural language processing advanced, more complex models like GloVe and FastText were developed. However, it was with the arrival of larger language models, such as BERT in 2018, that sentence embeddings began to be widely used. BERT introduced the idea of contextualization, where the meaning of a word can change depending on its context, leading to the creation of sentence embeddings that capture this semantic variability.
Uses: Sentence embeddings are used in various natural language processing applications, such as semantic search, where they allow finding relevant documents based on meaning rather than exact word matches. They are also fundamental in recommendation systems, where product or service descriptions can be compared. Additionally, they are used in text classification, sentiment analysis, and in generating automatic responses in chatbots, enhancing human-machine interaction.
Examples: An example of using sentence embeddings is in semantic search systems, where relevant results can be found even if the exact words do not match. Another example is in sentiment analysis applications, where user opinions about products or services can be classified based on the meaning of the sentences. They are also used in machine translation platforms, improving the quality of translations by understanding the context of sentences.
