Dynamic Question Generation using NER with various Feature Extraction and NLP Techniques

Abstract

The purpose of the Automatic Question Generator is to produce novel questions from the given text that are both linguistically natural, semantically precise, and syntactically coherent. Unlike activities such as summarization and paraphrase, replies play a vital role in question-answering tasks. The creation of multiple-choice questions involves the use of distractions of high quality and the formulation of successful questions. This technology enables instructors to generate multiple-choice assessment questions including both right answers and distractors. An instructor may promptly evaluate a student's understanding of the material. This method enables students to assess their own comprehension level of the topic matter. This technique is beneficial for generating assessment papers for evaluation in the educational industry. Teachers may develop questions about their topics by duplicating or transferring one or more paragraphs. In this paper we proposed an dynamic question generation using Named Entity Recognition (NER), Natural Language Processing (NLP) based approach with conventional learning techniques. The proposed NLP-NER based approach is applicable on heterogeneous as well as large text dataset. However, a compilation of inquiries is generated, derived from the texts that were identified as noteworthy or informative. Various methods of inquiry often yield questions that are primarily focused on gathering factual information, such as inquiries about individuals, time, location, reasons, and details. A natural language processing programme facilitates the comprehension of language and spoken communication by machines. The approach dissects the material into its constituent elements, deciphers the meaning of the language, selects the appropriate actions, and ultimately delivers the content to the user in a comprehensible language.