Word embedding is a technique used in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.
Think of word embedding like a language translator for computers. It translates words into a language (in this case, a set of numbers) that the computer can understand and work with more efficiently.
Here’s a more detailed explanation: Traditional methods of text processing involve representing each word as a unique identifier, like a dictionary where each word has a unique number. However, this method doesn’t capture the relationships between different words. For instance, the words ‘king’ and ‘queen’ might be much closer in meaning than ‘king’ and ‘apple’, but in traditional methods, these relationships are not captured.
Word embedding techniques, such as Word2Vec and GloVe, address this by representing words in a high-dimensional space (usually a few hundred dimensions), where the position and distance between words indicate their semantic similarity. So in this space, ‘king’ and ‘queen’ would be closer to each other than ‘king’ and ‘apple’.
Moreover, word embeddings can also capture more complex relationships, like analogies. For instance, the relationship “man is to king as woman is to queen” can be captured in the embedding space.
These word embeddings are learned from large amounts of text data and are used as the input to many NLP tasks, such as text classification, sentiment analysis, and machine translation. They provide a way for machines to deal with words in a numerical and computationally efficient way while preserving their semantic meaning.
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