This is not about word2vec, an algorithm for learning vector representations of words. This is about learning vector representations of thoughts (ideas, concep

This is not about word2vec, an algorithm for learning vector representations of words. This is about learning vector representations of thoughts (ideas, concepts, etc.) in an unsupervised manner. For reference, two months ago some of Hinton's colleagues at the University of Toronto published a paper describing an unsupervised algorithm for learning vector representations of sentences: http://arxiv.org/pdf/1506.06726v1.pdf http://arxiv.org/pdf/1506.06726v1.pdf Once trained, this algorithm is given a sentence and asked to find similar sentences in a corpus of half a million sentences, and it produces impressive results. For example, when asked to find the sentence with most similar vector representation of sentence 1, it finds sentence 2: * Sentence 1 (sentence given to the trained algorithm): "He ran his hand inside his coat, double-checking that the unopened letter was still there." * Sentence 2 (sentence with most similar vector representation found by algorithm in corpus): "He slipped his hand between his coat and his shirt, where the folded copies lay in a brown envelope." The paper has more examples. The vector representations learned by this type of algorithm truly appear to be capturing the meaning of sentences. This looks promising. -- Edit: Code used in the paper I mention is/will be here: https://github.com/ryankiros/skip-thoughts https://github.com/ryankiros/skip-thoughts