How computers learn word meanings with word2vec
Have you wondered how your phone guesses what you mean? Simple idea: a computer looks at many sentences and learns which words hang out together.
The model turns words into tiny points in a space so similar words sit near each other.
It does this by trying to predict nearby words and then it changes those points when it guesses wrong.
Over time the points capture real word meanings: colors cluster, places group, verbs bunch together.
This lets a machine know that king and queen are related in a way, or that Paris and France belong close.
The process is fast and can learn from lots of text, so apps like search, translation, or smart replies get better.
It's kinda like teaching a friend by example, the friend make guess, you say right or wrong, and the friend adjust.
Not magic, just math that finds patterns in words and turns them into word vectors that carry meaning and help computers work with language.
Read article comprehensive review in Paperium.net:
word2vec Parameter Learning Explained
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