What is word embedding?
A technique to represent words as numerical vectors in a high-dimensional space
word embedding explained in plain English
Analogy
Think of word embedding like a map where each word is a location. Just as locations on a map have coordinates, words in a word embedding have numerical coordinates that define their position in a semantic space. This allows the computer to navigate and understand the relationships between words, just like how you can navigate a map to find the distance between two locations.
Example
For example, a word embedding model can be trained on a large corpus of text data, such as Wikipedia articles or books. The model can then be used to make predictions, such as recommending products based on a customer's search history or generating text summaries of long documents.
How is word embedding used?
Word embeddings are used in natural language processing tasks such as language translation, text classification, and sentiment analysis. They are also used in chatbots and virtual assistants to improve their understanding of human language.
Common misconceptions about word embedding
One common misconception is that word embeddings are a simple lookup table that maps words to numbers. However, word embeddings are actually complex models that capture subtle relationships between words and their contexts.
History
Word embeddings have been around since the 1980s, but they gained popularity in the 2010s with the development of deep learning models such as Word2Vec and GloVe.
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