Feature completion in computer-human interactive learning

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;
• and
• training the machine-learning model with the dictionary as an input feature, wherein the training includes, for each data item in the first set of data items:
• for a first word position in the text stream within the data item, examining a window of text centered at a second word position in the text stream, wherein the window of text includes one or more words,
• utilizing a probability function to calculate a probability of the presence, at the first 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 window of text,
• determining an actual presence or absence, at the first word position, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, and
• modifying the probability function to adjust the probability in a positive or negative direction based on the determined actual presence or absence of the disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary.

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;
• 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.