Automatic Question Generator Using Natural Language Processing
AbstractThe Automatic Question Generator is intended to generate new questions from the text that are natural language, semantically accurate, and syntactically cohesive. In contrast to other natural language-generating tasks like summarization and paraphrasing, answers are crucial for questions. High-quality distractions and effective questions are used in the construction of multiple-choice questions. With the use of this system, educators can create multiple-choice assessment questions that have correct answers and distracters. An educator can quickly assess a student's comprehension of the subject. This technique allows students to evaluate their own understanding level of the subject. This model is useful for creating test papers for evaluation in the educational sector. By simply copying or pasting one or more paragraphs, teachers can generate questions on their subjects. Python programs that work with data from natural human language can be written with a Natural language tool kit. This tool kit introduces text processing libraries containing functions for tokenization, parsing, lemmatization, chunking, POS tagging, and stemming. Text is used by many natural language processing techniques, such as topic modeling, to identify important information. After that, a list of questions is created based on the texts that were extracted as being significant or instructive. Different ways to question generating typically provide questions that are factual in nature, such as who, when, where, why, and what. A program for natural language processing aids in the understanding of language and spoken language by machines. The method breaks down the content into its component pieces, interprets the language's meaning, chooses the relevant actions, and finally presents the content to the user in a language they can understand.
2022-12-31 — Updated on 2022-12-31
How to Cite
Puneeth Thotad , Shanta Kallur , Sukanya Amminabhavi. (2022). Automatic Question Generator Using Natural Language Processing. Journal of Pharmaceutical Negative Results, 2759–2764. https://doi.org/10.47750/pnr.2022.13.S10.330