1. A method of determining relevance of an electronic advertisement to a target content, said method comprising: extracting a set of terms from said electronic advertisement and said target content; calculating a first content match feature using said set of terms, the first content match feature comprising a translation evaluation feature indicating a degree to which n-grams of the electronic advertisement and the target content match; calculating a second content match feature using said set of terms, the second content match feature comprising a translation probability feature indicating a probability that one or more terms of the electronic advertisement are related to one or more terms of the target content, wherein the one or more terms of the electronic advertisement do not match the one or more terms of the target content; and processing said first content match feature and said second content match feature with a machine learning model to output a relevance score indicating the relevance of the electronic advertisement to the target content, the machine learning model comprising individual weights for the first and second content match features, the machine learning model being trained using said first and second content match features and machine learning techniques.