Open AccessSubjective Answer Evaluator
Author(s) -
Sarthak Kagliwal
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39090
Subject(s) - automatic summarization , computer science , natural language processing , artificial intelligence , similarity (geometry) , semantic similarity , matching (statistics) , ontology , information retrieval , image (mathematics) , statistics , mathematics , philosophy , epistemology
The automatic assessment of subjective replies necessitates the use of Natural Language Processing and automated assessment. Ontology, semantic similarity matching, and statistical approaches are among the strategies employed. But most of the methods are based on an unsupervised approach. The proposed system uses an unsupervised method and is divided into two modules. The first one is extracting the essential data through text summarization and the second is applying various Natural Language models to the text retrieved from the above step and giving marks to them. Keywords: Automatic Evaluation, NLP, Text Summarization, Similarity Measure, Marks Scoring