Automatic Evaluation of Descriptive Answers Using NLP and Machine Learning.

Abstract

The answer script analysis is a crucial a part of assessing student’s performance. Typically, an answer script analysis is finished manually that typically are often biased. The analysis depends on varied factors like mood swing of the authority, the inter-relation between the student and authority. To boot, analysis could be a very tedious and long task. During this paper, a linguistic communication processing-based methodology is shown for automatic answer script analysis. Our experiment consists of text extraction from answer script, measuring various similarities between summarized extracted text and hold on correct answers, so assign a weight value to every calculated parameters to attain the solution script. For outline generation from the extracted text, we've got used keyword-based summarisation techniques. Here four similarity measures (Cosine, Jaccard, Bigram, and Synonym) square measure used as parameters for generating the ultimate mark. Automatic analysis of answer scripts has been found terribly helpful from our experiments, and infrequently the assigned marks is that the same as manually scored marks.