7 Matching Annotations
- Dec 2019
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nlpoverview.com nlpoverview.com
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The context words are assumed to be located symmetrically to the target words within a distance equal to the window size in both directions.
O que significa dizer "simetricamente localizadas" as palavras alvo?
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- Apr 2017
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www.tensorflow.org www.tensorflow.org
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J(t)NEG=logQθ(D=1|the, quick)+log(Qθ(D=0|sheep, quick))
Expression to learn theta and maximize cost and minimize the loss due to noisy words. Expression means -> probability of predicting quick(source of context) from the(target word) + non probability of sheep(noise) from word
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Algorithmically, these models are similar, except that CBOW predicts target words (e.g. 'mat') from source context words ('the cat sits on the'), while the skip-gram does the inverse and predicts source context-words from the target words. This inversion might seem like an arbitrary choice, but statistically it has the effect that CBOW smoothes over a lot of the distributional information (by treating an entire context as one observation)
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levyomer.files.wordpress.com levyomer.files.wordpress.com
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arg maxvw;vcP(w;c)2Dlog11+evcvw
maximise the log probability.
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p(D= 1jw;c)the probability that(w;c)came from the data, and byp(D= 0jw;c) =1p(D= 1jw;c)the probability that(w;c)didnot.
probability of word,context present in text or not.
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Loosely speaking, we seek parameter values (thatis, vector representations for both words and con-texts) such that the dot productvwvcassociatedwith “good” word-context pairs is maximized.
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In the skip-gram model, each wordw2Wisassociated with a vectorvw2Rdand similarlyeach contextc2Cis represented as a vectorvc2Rd, whereWis the words vocabulary,Cis the contexts vocabulary, anddis the embed-ding dimensionality.
Factors involved in the Skip gram model
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