# Semantic Similarity Analysis for Examination Questions Classification Using WordNet
## 2 Related Work
More training sets resulted in more accurate results. Further, many researchers have proposed further investigations into semantic knowledge in question classifications to improve the accuracy of classification. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multicolumn{3}{c}{**Cognitive Verb List of Anderson Taxonomy**} \\ \cline{2-3}  & **Description** & **Verb List** \\ \hline \multirow{2}{*}{Creating} & Builds a structure or pattern from & categorizes, combines, compiles, composes, creates, devises, designs, explains, generates, modifies, organizes, plans, rearranges, reconstructs \\ \hline \multirow{2}{*}{Evaluating} & Makes judgments about the value of ideas & appraises, compares, concludes, contrasts, criticizes, critiques, defends, describes, discriminates, evaluates, explains, interprets \\ \hline \multirow{2}{*}{Analyzing} & Separates material or concepts into & analyses, breaks down, compares, contrasts, diagrams, deconstrracts, differentiates, discriminates, distinguishes, identifies, illustrates \\ \hline \multirow{2}{*}{Applying} & Uses a concept in a new situation or & applies, changes, computes, constructs, demonstrates, discovers, manipulates, modifies, operates, predicts \\ \hline \multirow{2}{*}{Understanding} & Comprehens the meaning, and & comprehends, converts, defends, distinguishes, estimates, explains, extends, generalizes, gives an example, infers, interprets, paraphrases \\ \hline \multirow{2}{*}{Remembering} & Recalls or retrieves previously learned & defines, describes, identifies, knows, labels, lists, matches, names, outlines, recalls, recognizes, reproduces, selects, states \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adapted from [PERSON]’s revisions on [PERSON]’s taxonomy verbs [7]. 2015, [PERSON], [PERSON], [PERSON], [PERSON] & [PERSON], I. [PERSON] et al. [3] proposed rule-based examination question classifications with WordNet and the cosine similarity algorithm. Verbs were extracted from the questions and similarities were identified by comparing them with BT verbs. Only a 71% accuracy was obtained. However, this research focused on one specific area only. [PERSON] and [PERSON] [13] proposed the classification of questions according to BT using the term frequency-inverse document frequency (TF-IDF) and Word2 Vec. Modified TF-IDF was used to identify the most important word in the questions. Word2 Vec was used to denote the identified verbs and BT verbs with a semantic similarity. Finally, the classifier provided an average accuracy of 89.7% [6]. However, this research only focused on single-sentence questions. [PERSON] et al. [14] studied the integration of statistical and machine learning in essay question classifications. TF-IDF and SVM were used in the question classification. However, this system could only process the questions with BT keywords. They obtained 82.60% of the F1 measurement. Table 2 summarizes the approaches and techniques used in examination question classifications and their performances. Overall, rule-based approaches were commonly used in examination question classifications. However, these questions were restricted to certain syntactic patterns, and more rules were set to achieve better accuracy. It was not easy to maintain a huge set of rules. Thus, ML was proposed by researchers to achieve better results in examination question classifications. The SVM has been the popular approach used in examination classifications. However, this accuracy can only improve with a large training set of examination questions. In general, examination question classifications have been studied by many researchers in recent years. However, there has been no implementation carried out from the research and no public dataset exists that contains real examination questions tested in a question classification system. Much research has focused on text pre-processing and classification using rule-based or machine learning with single sentences or one subject area.