6. A method of feature completion for machine learning, 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.
6. A method of feature completion for machine learning, 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. 7. The method of claim 6 , wherein modifying the function to adjust the calculated second probability includes A) modifying the function to increase the calculated second probability when the word at the given word position is in the dictionary, and B) modifying the function to decrease the calculated second probability when the word at the given word position is not in the dictionary.