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by wayfarer2s
4048 days ago
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I think you are right in that other inputs are needed to decipher meaning. Humans for example tend to have quite a lot of different sources of input -- as when we are children and learning new words we have the spelling (visual), how it sounds (auditory), and possibly another image that shows what the thing means ("cat"). Or maybe we have the auditory ("mommy") and the visual (the child's mother). If you were trained strictly on text, then the meaning of concepts is harder to decipher. It might be why abstract concepts like higher level math are hard for a lot of people to grasp -- their only exposure to the concepts is usually just in the form of text. As an exercise, when I think of the word "circle", images of circles and spheres show up in my head. Also the equation of a circle. My quick definition of it would be "a perfectly round object" which leads to questions of what "round" and "perfect" mean. The more I think about it, all my knowledge seems quite circular in that there are no axiomatic concepts, everything is relative and it just builds on itself. I wonder if that's the key to decipher meaning, increase the connections of the web -- with strong enough references you can pinpoint which of the nodes in the web something refers to. |
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In the case of this article, the NN isn't being asked to do any abstract task like "decipher meaning", but the very concrete task of "predict the next word". As the article shows NNs can do this fairly well.
There is also a evidence that they can learn very high level knowledge about words and objects. See the success of word vectors: http://technology.stitchfix.com/blog/2015/03/11/word-is-wort...