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前言

对word的representation要比对image的representation难,因为word是具有非常广泛的distribution,它的组合是非常之多的,但是image则非常集中;

在NLP领域一个非常重要的问题就是如何来度量语义的相似性;

representation of word

Vector space model

Distributional semantics

Word embedding中有这样的一段话:

In 2000 Bengio et al. provided in a series of papers the "Neural probabilistic language models" to reduce the high dimensionality of words representations in contexts by "learning a distributed representation for words". (Bengio et al., 2003).[12]