# Semantic Similarity Analysis for Examination Questions Classification Using WordNet
## 1 Introduction  
Assessments via written examinations are one of the most common methods used to evaluate students' achievement in most educational institutions. They evaluate students' achievements referring to the course learning outcomes (CLO) that are aligned with the level of learning and understanding based on Bloom's Taxonomy (BT). Therefore, it is crucial to prepare a high-quality examination question that can align with the intended CLO. The correctness of examination question classification is checked by the accreditation bodies and quality organizations [1]. However, there are always discrepancies in examination question classifications into BT due to the inefficiency of manual classification. Some BT verbs can be found in different BT levels that cause inconsistent labeling when examination questions are being classified. Although discrepancies in examination question classifications can be reduced with many rounds of manual moderation, this is time-consuming, and a good deal of effort must be put into ensuring the understanding of moderators in question classification.  
In recent years, various studies have been conducted on the application of natural language processing (NLP) in automation question classification (AQC). Humans canclassify examination questions easily based on their understanding of the meaning of the questions and BT. However, it is not easy to teach the machine to understand the meaning of these questions. Questions have to be processed in order to be understood by machines. NLP has been implemented in many fields, such as text classification, document summarization, question answering, etc. It enables the machine to read the text, interpret it and determine the important keywords. NLP has the computer ability to process information from a large database of text with very little human intervention [2]. Generally, question classification systems using NLP consist of three main modules: the pre-processing module, the keyword extraction module, and the classification module.  
This paper proposes a question classification system (QCS) that performs examination question classification based on the revised BT using semantic similarity techniques. This study started with an overview of commonly used tools and techniques in a pre-processing module and was then followed by semantic similarity analysis to improve the accuracy of the classification of the questions. The test results were analyzed, the best-performing methods or approaches adopted, and the possible enhancement of QCS was discussed. Other than NLP, multiple-sentence examination questions were included in this study. Many studies of examination question classifications are performed on single-sentence examination questions only and in specific areas only. Even in question-answering systems, this classification also involves mostly single sentences and not multiple sentences. However, in reality, examination questions consist of single sentences and multiple sentences, especially for examination questions with a higher thinking order that consists of problem statements or a case study with many sentences. Consequently, this QCS could benefit institutions of higher learning (IHL) in the development and implementation of question classification systems.  
Furthermore, this research studied examination question classifications from different areas, including Engineering Schools such as physics, programming, electronics, mechanical, management, etc. According to the research [3; 4; 5; 6], with rule-based and machine learning for question classification, accuracy can be improved with a huge set of rules implemented or a large training set to train the machine. In this research, 83% accuracy in 200 examination question classifications from different areas involving single and multiple sentences was achieved with only three rules in NLP text processing and optimization in semantic similarity analysis.