## 5 Experimental Results
The superiority of the proposed model relies on the fact that recurrent neural network is not considered the best kind of neural network for all natural language processing tasks, which perform poorly on sentences with lots of words [75]. The reason is, if the sequence is a sentence of five words, the network will contain one layer for each word, which would require heavy processing and take a lot of time. Moreover, the proposed \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**AEE Systems** & **DS1** & **DS2** & **DS3** & **DS4** & **DS5** & **DS6** & **DS7** & **DS8** & **Average** & **Rank** \\ \hline PEG & 0.84 & 0.72 & 0.84 & 0.86 & 0.83 & 0.82 & 0.85 & 0.75 & 0.81 & 2 \\ e-rater & 0.83 & 0.70 & 0.83 & 0.83 & 0.80 & 0.79 & 0.81 & 0.71 & 0.78 & 3 \\ Lexile & 0.69 & 0.63 & 0.68 & 0.61 & 0.65 & 0.66 & 0.59 & 0.64 & 0.64 & 4 \\ Proposed Model & 0.94 & 0.82 & 0.89 & 0.87 & 0.90 & 0.83 & 0.89 & 0.78 & 0.83 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed model with other state-of-the-art commercial systems in terms of quadratic weighted Kappa, attained on different datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**AEE Systems** & **DS3** & **DS4** & **DS5** & **DS6** \\ \hline Neural Network based model (CO-ATTN) & 0.70 & 0.81 & 0.80 & 0.82 \\ Proposed Model & 0.87 & 0.86 & 0.91 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between the proposed model and neural network-based AEE model on selected dataset in terms of _Kp_. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Jaccard Coefficient** & **Dice Coefficient** & **Cosine Coefficient** & **Proposed Model** \\ \hline DS1 & 0.71 & 0.77 & 0.79 & 0.93 \\ DS2 & 0.32 & 0.35 & 0.43 & 0.83 \\ DS3 & 0.25 & 0.28 & 0.43 & 0.87 \\ DS4 & 0.43 & 0.51 & 0.63 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between the proposed Model Similarity Measures-based AEE models in terms of _Kp_. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AutoSAS** & **Proposed Model** \\ \hline DS1 & 0.83 & 0.92 \\ DS2 & 0.82 & 0.82 \\ DS3 & 0.85 & 0.87 \\ DS4 & 0.84 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between Word Graph Model (AutoSAS) and the Proposed Model in terms of _Kp_. model utilizes more than one semantic attribute to check similarity. In addition to that, the proposed model exceeded the models that use similarity measures because it utilized LSA to overcome the drawbacks of similarity measures. Similarity measures cannot deal with synonyms (words with the same meaning) or polysemy (the same word with different meaning). Using LSA transformed the essay into semantic space, so every word was treated as a point in the space, and as a result, it was judged semantically well. The superiority of the proposed model over the algorithm that uses lexicon-semantic matching comes from transferring the essay into a semantic space and utilizing spatial measures to check the coherence and consistency of the points in this semantic space (the essay's parts). The AutoSAS system used word2 vector and doc2 vector techniques to extract features. These two techniques have a great drawback, which is that they do not define the sub-linear relationships explicitly. The purpose of experiment four (Essay Error Analysis) is to evaluate the suggested model's error detection capabilities. Sixty tourism-related phrases were constructed artificially. Forty-one statements were marked as correct and nineteen as erroneous to reflect the base truth. It was designed to test how well the automatic error detection model can identify incorrect statements using only a common sense neutrosophic ontology as input. Both the sensitivity (the percentage of incorrect sentences accurately identified as such) and the specificity (the percentage of correct sentences correctly identified as such) of the suggested model were evaluated. Putting the suggested model to the test, it was found to be both highly specific (100%) and highly sensitive (80.2%).