# Semantic Similarity Analysis for Examination Questions Classification Using WordNet
## 3 Methodology
### Question Classification  
The question classification (QC) module aims to classify questions based on the RW and KW extracted from the VE module, using WordNet similarity. The RW and KW are compared with the verbs listed from BT. Each verb has a set of synsets. The WordNet similarity identifies how similar the two sets of synsets--a set from the RW and KW--are before utilizing another set from the verbs listed from BT. The similarity score is in the range of 0 to 1, where the higher similarity score obtained means that the synsets are closer to each other in terms of word sense. Generally, the algorithm for the QC module based on WordNet similarity can be grouped into three possible scenarios. The first scenario is the exact matching between the RW and one of the BT verbs (BV). In this scenario, the question can be directly classified according to the BV. The second scenario is when the RW matches with exactly one of the BVs, and the BV exists in multiple BT levels. The QC requires WordNet similarity between the KW and BV for classification. The third scenario is where no exact matching occurs, and the WordNet similarity score between the RW, KW and BV is used in classification. Each RW, KW and BV from BT level 1 to level 6 has its own synset, so a huge number of synset similarity scores can be generated. A three-dimensional matrix was designed to summarize the scores and tabulate them into each Bloom level. The method used to summarize the matrix was evaluated, with the best result implemented in the QC module. QC identifies the BT level with the highest similarity score as the BT level of the questions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Input Sentence** & **For the Wye-Wye Circuit in Figure 2, Calculate the Line Currents.** \\ \hline \multirow{4}{*}{After POS Tagging, Stop Words and Punctuation Removal} & [(‘for’, ’IN’), (‘vye-wye’, ’J’), (‘circuit’, ’NN’), (’figure’, ’NN’), (’2’, ‘CD’), (**‘calculate’, ’VBP’), (’line’, ’NN’), (‘currents’, ’NN’)] \\  & \(\bullet\) No ’VB’ tagged verb. \\  & \(\bullet\) There is a ’VBP’ tagged verb—‘calculate’. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario of no verb form with “VB” tag but with other verb forms such as “VBP” in a single-sentence question.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Input Sentence** & **Write a C Program to Find the Number of Bytes Required to Store** \\ \hline \multirow{4}{*}{After POS Tagging, Stop Words and Punctuation Removal} & [(‘write’, ’VB’), (‘c’, ’NN’), (‘program’, ’NN’), (‘find’, ’VB’), (’number’, ’NN’), (’bytes’, ’NN’), (‘required’, ’VBN’), (‘store’, ’VB’), (‘integer’, ’NN’), (‘int’, ’NN’), (‘data’, ’NN’), (’type’, ’NN’), (’variable’, ’NN’)] \\  & \(\bullet\) 3 verbs tagged with ’VB’—write, find and store. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scenario of more than one verb tagged with the “VB” tag in a single-sentence question.  
As for the multi-sentence type of questions, the questions were tokenized into single sentences such as S1, S2, etc. Every single sentence was processed in the same manner. QC identified the BT level with the highest similarity score as the BT level of every single sentence. The highest BT level among the sentences was then identified as the BT level of the multi-sentence type of question. For example, S1 was classified as BT Level 2, while S2 was classified as BT Level 3. The multi-sentence type of question was then classified to the highest BT level: BT Level 3. The highest BT level selection in the multi-sentence questions was consistent with the manual question classification practice.