Word embeddings are vector representations of words and phrases, used for natural language processing (NLP) tasks, such as sentiment analysis, automatic summarization, and machine translation. Word embeddings help machines understand the context of words, enabling them to process human language with greater accuracy and speed.
Word2Vec is the most widely used word embedding technique. It was developed by Google researchers in 2013, and maps words and phrases to a vector of numbers. Word2Vec uses a shallow neural network to analyze text and understand the context in which a word is used.
GloVe is another popular word embedding technique. It was developed by Stanford researchers in 2014, and uses a global matrix factorization approach to map words and phrases to a vector of numbers. GloVe is based on a statistical co-occurrence model which learns word relationships from a large corpus of texts across different languages and topics.
FastText is another type of word embedding technology developed by Facebook AI research division in 2016. It is particularly well-suited to modeling short phrases and understanding how they are related to other words, phrases, and topics. FastText uses a shallow neural network to group similar words together to better represent their context and meaning.
Word embeddings play an important role in NLP applications and are frequently used in machine learning algorithms. They enable machines to understand the context in which words are used, leading to better accuracy and faster processing times for natural language tasks.
