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mind:word2vec [2016/09/25 16:27] bayb2 |
mind:word2vec [2016/09/25 16:42] bayb2 |
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| =Word2Vec= | =Word2Vec= | ||
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| + | ==Intro== | ||
| + | “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 | ||
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| + | 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. | ||
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| + | Word2Vec is not classified as "deep learning" because it is only a 2-layer neural net. | ||
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| + | ===Examples=== | ||
| + | |||
| + | 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] | ||
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| ==Notation== | ==Notation== | ||
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| - | |||
| - | |||
| - | Word2Vec is a 2-layer neural net | ||
| Input -> text corpus | Input -> text corpus | ||
| Output -> set of vectors (neural word embeddings) | Output -> set of vectors (neural word embeddings) | ||
| - | Can guess a word’s association with other words, or cluster documents and define them by topic. | + | |
| More research: Cosine similarity, dot product equation | More research: Cosine similarity, dot product equation | ||
| ==Models== | ==Models== | ||
| - | ===Continuous bag of words model=== | + | ===Continuous bag of words (CBOW) model=== |
| - | Using context to predict a target word. Faster. | + | Uses a context to predict a target word. Faster. |
| ===Skip-gram model=== | ===Skip-gram model=== | ||
| - | Using a word to predict a target context. Produces more accurate results on large datasets. | + | Uses a word to predict a target context. Produces more accurate results on large datasets. |
| - | “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.” | ||
| Each word is a point in a 500-dimensional vectorspace. | Each word is a point in a 500-dimensional vectorspace. | ||
| - | Qualities become quantities, and similar things and ideas are shown to be “close”. | + | |
| More than three layers in a neural network (including input and output) qualifies as “deep” learning. Deep means more than one hidden layer. | More than three layers in a neural network (including input and output) qualifies as “deep” learning. Deep means more than one hidden layer. | ||
| Line 43: | Line 59: | ||
| - | ==Examples== | ||
| - | |||
| - | 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] | ||
| ==Links== | ==Links== | ||
| *http://deeplearning4j.org/word2vec | *http://deeplearning4j.org/word2vec | ||
