Keyword Extraction Using Latent Semantic Analysis For Question Generation

G Deena; K. Raja

doi:10.6180/jase.202304_26(4).0006

Keyword Extraction Using Latent Semantic Analysis For Question Generation

Computer Science and Information Engineering

G. Deena This email address is being protected from spambots. You need JavaScript enabled to view it.^1,2 and K. Raja²

¹Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, 600119, India
²Department of Computer Science and Engineering, SRM Institute of Science and Technology Bharathi Salai, Chennai, 600089, India

Received: December 10, 2021
Accepted: May 28, 2022
Publication Date: July 20, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202304_26(4).0006

ABSTRACT

In-Text Mining, Information Retrieval (IR), and Natural Language Processing (NLP) dig out the important text or word from an unstructured document is coined by the technique called Keyword extraction. It helps to identify the core information about the document in specific. Instead of going through the entire document, this method helps to retrieve sufficient information instantly in a short span of time. It is essential to mine the meaningful word from the document in text analytics. The proposed system has been based on semantic relation to extracts the keyword from unstructured text documents by means of practice like Latent Semantic Analysis (LSA). In view of this method, there exists a semantic relation between the sentences available in the document and the words. Extracted text permits to signify text in a strong way and has a high preference to carry more important information about the sentences. In this regard, LSA has produced better outcomes when compared with the TF-IDF, RAKE, YAKE, and Text Rank algorithm. Consequently, the keyword extraction has been occupied in Automatic Question Generation (ACQ) to generate the Fill up the blank (FB) and Multiple Choice Questions (MCQ) with distractor set. The top five, ten keywords are involved in questionable generation. The proposed system could be implemented in the question generation system to assess the skill level of the learner.

Keywords: Natural Language Processing, Latent Semantic Analysis, Multiple Choice Questions, Keyword Extraction, Semantic relation

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