14. One or more hardware computer-storage media having embodied thereon computer-usable instructions that, when executed, facilitate a method of feature completion for machine learning, the method comprising: storing a first set of data items, wherein each data item includes a text stream of words; providing a dictionary, wherein the dictionary includes a list of words that define a concept usable as an input feature for training a machine-learning model to score data items with a probability of being a positive example or a negative example of a particular class of data item; providing a feature that is trained to calculate a first probability of a presence, within a stream of one or more words, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; utilizing the feature to determine the first probability of the presence, within a stream of one or more words, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary at a given word position in the data item; providing a machine-learning model that is trainable to calculate a second probability of the presence, within the stream of one or more words at the given word position, of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, based on one or more words in the data item not utilized by the feature to determine the first probability; utilizing the machine-learning model to determine the second probability of the presence, within the stream of one or more words at the given word position, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, based on the one or more words in the data item not utilized by the feature to determine the first probability; determining an actual presence or absence, at the given word position, of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; and modifying the machine-learning model to adjust the second probability in a positive or negative direction based on the determined actual presence or absence of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; wherein the feature determines one or more of: whether any words from a given list appear at the center of a window of text around the given word position in which center words in the window of text have been removed, a presence or absence of a verb in the window, a presence or absence of a noun followed by an adjective, or a number of occurrences of a given word in the window.
15. The media of claim 14 , wherein the feature is a regular expression operating over strings to predict semantically matching positions in text within a string at each considered position, while the machine-learning model input includes a window of text around the considered position in which center words in the window of text have been removed.