Abstract: Unsupervised machine scoring of free-response answers can be provided, eliminating the need to create a model answer. A scoring system can receive a set of free-response answers with associated response content and determine, from all the associated response content, a commonality content by identifying semantically related response content from the set of free-response answers. For each free-response answer, the scoring system can determine an amount of similarity between the associated response content for that free-response answer and the commonality content and assign a similarity value from the amount of similarity to that free-response answer. The amount of similarity indicates a degree of “correctness” of an answer and, according to an implementation, can be considered to be the distance of an embedding of a free-response answer from a vector-related average of all the free-response answers.

Abstract: Unsupervised machine scoring of free-response answers can be provided, eliminating the need to create a model answer. A scoring system can receive a set of free-response answers with associated response content and determine, from all the associated response content, a commonality content by identifying semantically related response content from the set of free-response answers. For each free-response answer, the scoring system can determine an amount of similarity between the associated response content for that free-response answer and the commonality content and assign a similarity value from the amount of similarity to that free-response answer. The amount of similarity indicates a degree of “correctness” of an answer and, according to an implementation, can be considered to be the distance of an embedding of a free-response answer from a vector-related average of all the free-response answers.