skip-thoughts

I know you meant "for producing spam" but it seems these techniques can also do semantic-relatedness[1] which I imagine could answer questions like: "Is this about viagra?" or "Is this related to something the mailbox owner actually talks about?" [1] https://github.com/ryankiros/skip-thoughts#semantic-relatedn...

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 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