SEMANTIC ANALYSIS OF LONG ANSWERS

Abstract

The inspiration for this research paper was the natural bias in university paper checking. When a paper is checked it is either checked by a professor who teaches the subject or someone who has no knowledge of the subject. When checked by the latter type, the answers cannot be appropriately marked unless obviously highlighted. This paper aims to check long answers without human intervention using artificial intelligence and regular expressions. It checks student or examinee written digital form answer by comparing it to an answer key which is to be provided by the exam host. The proposed methodology allows doing so by combining two techniques to get a faster and more accurate system to check long answers. The long answers will be evaluated by breaking them to simplest form of sentences and then encoding them to high density vectors using a Deep Averaging Network (DAN) to analyses the semantic similarity of the examinees answer to the provided answer key. This system does not look for only keywords in the content of the answer but looks at the sentence as a whole and if it evaluates similarly to the content in the answer key. This research relies on the availability of an answer key to check answers and does not check the relevance of content written by the examinee, meaning as long as examinee writes points mentioned in the answer key, he/she will be marked correct. This system of evaluation doesn't cut marks for wrong point (meaning no negative marking).