Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for generating distractors for text-based multiple choice test (MCT) items, the method comprising: receiving, by a computer, an MCT item stem; transmitting, by a computer, the stem to a question answering (QA) system; receiving, by a computer, from the QA system a plurality of candidate answers that relate to the stem; identifying, by a computer, incorrect candidate answers in the plurality of candidate answers; extracting, by a computer, textual features from the stem; generating, by a computer, a set of semantic criteria associated with the extracted textual features; and selecting, by a computer, a subset of the incorrect candidate answers, based on the generated semantic criteria. 2. A method in accordance to claim 1, wherein the stem is one of: a question to be answered, a problem to be solved, or an incomplete statement to be completed. 3. A method in accordance to claim 1, wherein the MCT item includes a predefined number of alternative choices, and the plurality of candidate answers is at least the predefined number of alternative choices and less than a predefined maximum number. 4. A method in accordance to claim 1, wherein receiving, by a computer, an MCT item stem further comprises: receiving, by a computer, an MCT item stem and key; and wherein identifying incorrect answers comprises: identifying a correct answer by comparing the plurality of candidate answers with the key; and deleting the correct answer from the plurality of candidate answers. 5. A method in accordance to claim 1, wherein generating the set of semantic criteria associated with the extracted textual features comprises: applying a trained machine learning model to the extracted textual features, using a predefined set of semantic criteria; and refining the set of semantic criteria by applying a predefined set of rules. 6. A method in accordance to claim 5, wherein applying a trained machine learning model further comprises using multi-label classification. 7. A method in accordance to claim 1, wherein selecting a subset of the incorrect candidate answers, based on the generated semantic criteria, comprises: generating hypotheses concerning the semantic criteria and the incorrect candidate answers; collecting evidence supporting or refuting each hypothesis; assigning to each statement a numerical value representing a degree to which the evidence justifies or refutes the hypothesis; and selecting incorrect candidate answers based on the numerical value.