个人技术笔记

Thought Vector

发布时间：2016-03-10  栏目：人工智能, 深度学习, 自然语言处理  评论：0 Comments

Thought Vector可以应用在人工智能、自然语言理解等领域。

 

A thought vector is like a word vector, which is typically a vector of 300-500 numbers that represent a word. A word vector represents a word’s meaning as it relates to other words (its context) with a single column of numbers.

That is, the word is embedded in a vector space using a shallow neural network like word2vec, which learns to generate the word’s context through repeated guesses.

A thought vector, therefore, is a vectorized thought, and the vector represents one thought’s relations to others. A thought vector is trained to generate a thought’s context. Just as a words are linked by grammar (a sentence is just a path drawn across words), so thoughts are linked by a chain of reasoning, a logical path of sorts.

So training an algorithm to represent any thought in its relation to others might be called the artificial construction of common sense. Given one thought, a neural network might predict the thoughts that are likely to follow, much like recurrent neural networks do with characters and words. Conversation as search.

 

More details at: http://deeplearning4j.org/thoughtvectors

 

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