Word embeddings (Word2Vec, GloVe, FastText)

Word embeddings are a feature representation technique used in natural language processing (NLP) and machine learning (ML) algorithms. Word embeddings map words in a corpus or document to numerical vectors of real numbers. These numerical vectors can capture semantics and context of words in a given text. Word embeddings are used to improve the accuracy and performance of NLP and ML algorithms.

Word2Vec is a type of word embeddings developed by a group of researchers at Google in 2013. Word2Vec uses a neural network to learn the underlying structure of words in a document or corpus. Word2Vec produces high-quality word embeddings as compared to traditional methods such as one-hot encoding.

Another type of word embeddings is GloVe, which stands for Global Vectors for Word Representation. GloVe was developed in 2014 by a team of Stanford researchers. GloVe can capture global and local semantics from large-scale corpora. GloVe has been used to improve the performance of many NLP and ML algorithms.

FastText is a type of word embeddings developed in 2016 by a team of Facebook AI researchers. FastText differs from Word2Vec and GloVe in that it uses subword level information. This allows FastText to capture morphological information about words, which can result in better performance for out-of-vocabulary words.

In conclusion, Word embeddings are becoming increasingly important in natural language processing and machine learning. Word2Vec, GloVe, and FastText are popular types of word embeddings that are used to improve the accuracy and performance of NLP and ML algorithms.