9. A method of measuring a probability that an out-of-document phrase is relevant to a document that does not contain the out-of-document phrase, the method comprising: receiving as input the out-of-document phrase not present in the document; extracting in-document phrases from the document and measuring probabilities that the in-document phrases are relevant to the document, respectively, the in-document phrases comprising phrases in and extracted from the document, the probabilities being computed according to the in-document phrases and the document; representing the in-document phrases as respective term vectors, each term vector comprising elements, where each term element comprises a term and a weight value computed for the term based on the term and the document; representing the out-of-document phrase as a term vector comprised of elements, where each element comprises a term and a weight value computed for the term; using the term vectors of the in-document phrases and their measured probabilities to train a regression model, or a classification model, or both; and predicting the probability by passing the term vector of the out-of-document phrase to the trained regression, model, or the classification model, or both and using an output thereof as a prediction of the probability.
9. A method of measuring a probability that an out-of-document phrase is relevant to a document that does not contain the out-of-document phrase, the method comprising: receiving as input the out-of-document phrase not present in the document; extracting in-document phrases from the document and measuring probabilities that the in-document phrases are relevant to the document, respectively, the in-document phrases comprising phrases in and extracted from the document, the probabilities being computed according to the in-document phrases and the document; representing the in-document phrases as respective term vectors, each term vector comprising elements, where each term element comprises a term and a weight value computed for the term based on the term and the document; representing the out-of-document phrase as a term vector comprised of elements, where each element comprises a term and a weight value computed for the term; using the term vectors of the in-document phrases and their measured probabilities to train a regression model, or a classification model, or both; and predicting the probability by passing the term vector of the out-of-document phrase to the trained regression, model, or the classification model, or both and using an output thereof as a prediction of the probability. 11. A method according to claim 9 , wherein probabilities of in-document phrases are generated by a keyword extraction algorithm.