1. One or more computer-readable 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 the 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 the 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, 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, and D) modifying the function to adjust the calculated second probability, based on the calculated first probability.