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@article{169294,
        author = {Prasad Dattatray Jagdale and Prof. B. C. Tandale and Prathamesh Pandhare and Wrushabhkumar Bangar and Pritam Jadhav},
        title = {Subjective Answer Evaluation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {626-628},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169294},
        abstract = {Evaluating subjective papers manually is often tedious, time-consuming, and inconsistent. Unlike objective tests, subjective answers require in-depth analysis as they are open-ended and vary significantly in structure and length. Traditional automated grading approaches, such as keyword matching, often fail to capture the full meaning and context of responses. This project addresses these challenges by leveraging machine learning and natural language processing (NLP) to automate subjective answer evaluation. We employ advanced techniques like Word2Vec, cosine similarity, Word Mover’s Distance (WMD), Naive Bayes, and BERT for deeper contextual understanding. This system evaluates answers based on content and semantics, improving grading accuracy and fairness while reducing time and effort.},
        keywords = {Natural Language Processing (NLP), Machine Learning (ML), Subjective Answer Evaluation, Automated Grading, Word2Vec, Word Mover's Distance (WMD), Naive Bayes, BERT.},
        month = {November},
        }