1. 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; accessing 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 already trained to determine a probability that a word at a given word position corresponds semantically to the concept defined by the words in the dictionary; and training the machine-learning model with the dictionary as an input feature, wherein the training includes A) for the given word position in a text stream within a data item, utilizing the provided feature to calculate a first probability that the word at the given word position corresponds semantically to the concept defined by the words in the dictionary, B) examining a context of the given word position, wherein the context includes a number of words preceding the given word position and a number of words following the given word position, and wherein the context does not include the word at the given word position, C) calculating a second probability that the word at the given word position corresponds semantically to the concept defined by the words in the dictionary, based on a function of the words in the context of the given word position, wherein calculating the second probability comprises one or more of: 1) determining whether any words from a given list appear at a center of a window of text around the given word position in which center words in the window of text have been removed, 2) determining a presence or absence of a verb in the window, 3) determining a presence or absence of a noun followed by an adjective, or 4) determining a number of occurrences of a given word in the window, and D) modifying the function to adjust the calculated second probability, based on the calculated first probability.