wordembedding

Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation. They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems. In this approach, words are represented by real-valued vectors in a predefined vector space. Each word is mapped to one vector and the vector values are learned in a way that resembles a neural network, and hence the technique is often lumped into the field of deep learning.

Word embeddings are useful because they capture the context of a word in a document, semantic and syntactic similarity, relation with other words, etc. Word embeddings can be used for various natural language processing tasks such as named entity recognition, sentiment analysis, machine translation, etc. Word2Vec, GloVe, and FastText are some of the popular techniques to learn word embeddings from text.

Word embeddings can be static or dynamic. Static word embeddings are fixed after training and do not change with the context. Dynamic word embeddings, on the other hand, can change with the context in which they are used. Contextualized word embeddings are a type of dynamic word embeddings that are learned using a neural network and can capture the context of a word in a document.