Computer Application for Assessing Subjective Answers using AI

Abstract

Finding the similarity and relatability between two pieces of text is often used in multiple places today. Search Engines, recommendations, web indexes, etc., make use of the similarity between two pieces of data to come to various different conclusions. However, no reliable system has been developed so far that takes into account the similarity between a student’s answer and the teacher’s model answer to give the most accurate and precise marks to the student. The major reason lies in the fact that such a system must be built on multiple parameters and hence the complexity of the system increases. However, on close examination, it could be found that there are various methods that work towards helping in the formulation of the final scores secured by the student based on the similarity between the words of the student answer and model answer as well as the similarity between the meaning of then sentences formed by the student and the model answers. These methods have been reviewed and the most efficiently working ones have been put forward in this proposed system.

Keywords: HMM (Hidden Markov Model), NLP (Natural Language Processing), ML (Machine Learning), CFG (Context Free Grammar), TF-IDF (term frequency- inverse document frequency), BoW (Bag of Words), Latent Semantic Indexing (LSI)