Subjective Answer Evaluation Using NLP
DOI:
https://doi.org/10.48001/978-81-966500-7-0-4Keywords:
Sentiment analysis, Logical flow, Language, EvaluationAbstract
An overview of the state of NLP techniques for assessing subjective responses is given in this abstract. Semantic analysis, sentiment analysis, and coherence verification are the three main areas of emphasis. Understanding the meaning hidden within the text is the goal of semantic analysis, and it can be accomplished with tools like knowledge graphs, word embeddings, and transformer models (BERT, GPT, etc.). Sentiment analysis evaluates the response’s subjective subtleties and emotional tone. The process of coherence checking guarantees the text’s consistency and logical flow. Because of the inherent heterogeneity in human language and the variety of ways that various people may convey the same notion, evaluating subjective replies is a difficult undertaking. Due to the heavy reliance of traditional assessment systems on human evaluators, issues with bias, scalability, and consistency arise. A potential remedy is provided by natural language processing (NLP), which gives methods and tools for automating and standardizing the evaluation process.
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