# Semantic Similarity Analysis for Examination Questions Classification Using WordNet
## 2 Related Work  
Various studies were carried out in the automation of examination question classifications based on BT using NLP. BT has been widely accepted and implemented as an important tool to guide academics, develop a holistic assessment, and promote a higher thinking order to design examination questions [1]. The revised BT consists of six cognitive levels--Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating, as shown in Table 1[7]. The verbs listed in BT are used as an indicator for question classification. However, some of the listed verbs exist in multiple BT levels [8], which often leads to inconsistencies when categorizing the examination questions by academics due to their different understanding of BT. Thus, an automated system is proposed to classify these questions using NLP. NLP enables machines to identify keywords from the questions and then classify them according to BT. Both [PERSON] and [PERSON] [9] and [PERSON] et al. [10] proposed an automated analysis of examination questions to determine the appropriate BT levels using a rule-based approach to the syntactic structure of the questions. Text pre-processing techniques such as stop-words removal, stemming, lemmatization and part-of-speech (POS) tagging have also been implemented. The [PERSON] and [PERSON] [9] approach achieved a satisfactory result with a macro F1 of 0.77. However, it required more rules and greater training questions to improve the accuracy of the system. On the other hand, [PERSON] et al. [10] proposed a reduction in the number of question categories to three categories in order to achieve a more satisfactory result. Reducing these question categories is not a good resolution to be implemented in examination question classifications with BT. Subsequently, the machine learning (ML) approach was explored to overcome the defect of rule-based classifications. The questions tagged with associated target responses were supplied to the machine prior to predicting the correct response. Based on the training dataset, the machine could classify this question automatically without many rules set when given a new set of data. [PERSON] et al. [11] suggested an automatic system to classify the Indonesian language based on the support vector machine (SVM) algorithm and achieved promising classification results with 88.6% accuracy. [PERSON] and [PERSON] [1] and [PERSON] et al. [12] also studied examination question classification with a SVM. The researchers compared different machine learning methods such as Naive Bayes, SVM, KHH logistic regression and decision trees in their study. However, this research only focused on computer science examination questions or single-sentence questions. [PERSON] and [PERSON]'s [1] research provided the best classification result with 0.7667 while [PERSON]'s [12] research obtained an accuracy of 75.2%. In our research, we aimed to include examination questions from different areas so that these could be implemented in different faculties later. Further, we would expect to have an accuracy greater than 80% in the real implementation of examination question classification. [PERSON] et al. [5] classified examination questions with semantic and syntactic methods. POS tagging was used to determine the verbs and nouns from the questions while WordNet was used to identify the required sense of the words and remove irrelevant information. Machine learning SVM was then used to classify the examination questions with an accuracy of 82%. However, this research only focused on a specific area which was computer programming with single-sentence questions only as the training dataset. Typically, ML was more flexible in question classifications than rule-based classifications because questions could be trained on a new taxonomy, while a good deal of time was spent maintaining the rules. However, it was challenging to obtain enough data for the experiment, especially collecting examination questions in order to improve the accuracy of examination classification.