1. A computer-implemented method of measuring probabilities that phrases are relevant to a document where document does not contain the phrases and the phrases comprise out-of-document phrases, the method comprising: providing a similarity measure that can measure similarity between two arbitrary strings; computing a first sparse word vector from the document, the first sparse word vector comprised of words extracted from the document and weights assigned thereto, respectively, each weight computed according to relevance to the document of a corresponding word in the first sparse word vector as computed by a term weighting function; and computing a second sparse word vector for one of the phrases by submitting the phrase to a search engine, receiving a search result from the search engine and comprising at least parts of documents determined by the search engine to be relevant to the one of the phrases, and generating the second sparse word vector from the at least parts of the documents in the search result provided by the search engine, the second sparse word vector comprising words obtained from the at least parts of the documents in the search and weights of the words, respectively; applying the similarity measure to the first sparse word vector and the second sparse word vector to derive a similarity score scoring similarity between the phrase and the document, wherein the similarity score is derived according to the words and weights in the first sparse word vector and according to the words and weights in the second sparse word vector; and mapping the similarity score to a corresponding probability using a learned sigmoid function.