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“Just as Van Gogh’s painting of sunflowers is a two-dimensional mixture of oil on canvas that represents vegetable matter in a three-dimensional space in Paris in the late 1880s, so 500 numbers arranged in a vector can represent a word or group of words.” –DL4J

Word2Vec can guess a word’s association with other words, or cluster documents and define them by topic. It makes qualities into quantities, and similar things and ideas are shown to be “close” in its 500-dimension vectorspace.

Word2Vec is not classified as “deep learning” because it is only a 2-layer neural net.


Rome - Italy = Beijing - China, so Rome - Italy + China = Beijing
king : queen :: man : woman
house : roof :: castle : [dome, bell_tower, spire, crenellations, turrets]
China : Taiwan :: Russia : [Ukraine, Moscow, Moldova, Armenia]


Algebraic notation

knee - leg = elbow - arm

English logic

knee is to leg as elbow is to arm

Logical analogy notation

knee : leg :: elbow : arm

Input → text corpus Output → set of vectors (neural word embeddings)

More research: Cosine similarity, dot product equation


Continuous bag of words (CBOW) model

Uses a context to predict a target word. Faster.

Skip-gram model

Uses a word to predict a target context. Produces more accurate results on large datasets.

Each word is a point in a 500-dimensional vectorspace.

More than three layers in a neural network (including input and output) qualifies as “deep” learning. Deep means more than one hidden layer.


Word2Vec can be implemented in DL4J, TensorFlow

mind/word2vec.1474843322.txt.gz · Last modified: 2016/09/25 16:42 by bayb2
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