What is word embedding in NLP?
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Word embedding is a technique used to represent words as dense, low-dimensional vectors in a continuous vector space, capturing semantic relationships and contextual information between words.
Word embedding in natural language processing (NLP) is a technique used to represent words as dense vectors of real numbers in a continuous vector space. This mapping allows words with similar meanings to have similar vector representations, capturing semantic relationships between words. Word embeddings are typically learned from large text corpora using neural network-based models such as Word2Vec, GloVe, or FastText. These models take into account the context in which words appear in the text to generate meaningful vector representations. Word embeddings are widely used in various NLP tasks, including language modeling, text classification, machine translation, sentiment analysis, and named entity recognition, among others. They enable algorithms to effectively process and understand natural language by capturing the semantic and syntactic properties of words.