Question answering system-based generation of distractors using machine learning
First Claim
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1. A method for generating distractors for text-based multiple choice test (MCT) items, the method comprising:
- receiving, by a first computer from a second computer over a network, in response to information entered into a user interface on the second computer, an MCT item stem and key;
submitting, by the first computer, the stem to a question answering (QA) system wherein a QA system is a computer system that, in response to a query, automatically generates a list of candidate answers to the query;
in response to submitting the stem to the QA system, receiving, by the first computer, from the QA system a plurality of candidate answers;
identifying, by the first computer, one or more incorrect candidate answers in the plurality of candidate answers;
extracting, by the first computer, textual features from the stem, wherein a textual feature is a term in the stem or a concept semantically related to a term in the stem;
applying, by the first computer, a machine learning model to generate a set of semantic criteria associated with the extracted textual features;
selecting, by the first computer, as distractors one or more of the incorrect candidate answers, that satisfy the generated semantic criteria;
creating, by the first computer, an MCT item that includes the stem, the key, and the distractors; and
transmitting, by the first computer, the created MCT item via the network to the second computer.
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Abstract
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.
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7 Claims
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1. A method for generating distractors for text-based multiple choice test (MCT) items, the method comprising:
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receiving, by a first computer from a second computer over a network, in response to information entered into a user interface on the second computer, an MCT item stem and key; submitting, by the first computer, the stem to a question answering (QA) system wherein a QA system is a computer system that, in response to a query, automatically generates a list of candidate answers to the query; in response to submitting the stem to the QA system, receiving, by the first computer, from the QA system a plurality of candidate answers; identifying, by the first computer, one or more incorrect candidate answers in the plurality of candidate answers; extracting, by the first computer, textual features from the stem, wherein a textual feature is a term in the stem or a concept semantically related to a term in the stem; applying, by the first computer, a machine learning model to generate a set of semantic criteria associated with the extracted textual features; selecting, by the first computer, as distractors one or more of the incorrect candidate answers, that satisfy the generated semantic criteria; creating, by the first computer, an MCT item that includes the stem, the key, and the distractors; and transmitting, by the first computer, the created MCT item via the network to the second computer. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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Specification