## 3 Methodology
### Preprocessing Module
\begin{table}
\begin{tabular}{c c} \hline \hline
**N-Gram Size** & **Sequence Examples** & **Types of Entities** \\ \hline
1-gram & [bread] & [food], [mouna], \(\ldots\) \\
2-gram & [bread and] & [\(\varnothing\)] \\
3-gram & [bread and butter] & [\(\varnothing\)] \\
4-gram & [bread and butter pudding] & [pudding], [food] \\
5-gram & [bread and butter pudding with] & [\(\varnothing\)] \\
6-gram & [bread and butter pudding with blueberries] & [\(\varnothing\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An example results of the types of entities. \begin{table}
\begin{tabular}{l c} \hline \hline
**Aspect** & **Synonymous Aspect Terms** \\ \hline waiter & waitress, bartender, waitperson, barkeeper, \\  & sommelier, steward, stewardess, headwaiter, \\  & \(\ldots\) \\ food & provisions, bread, meat, meal, fare, foodstuffs \\  & table, supplies, victuals, grub, chow, \(\ldots\) \\ views of city & views, view \\ bread and butter pudding with blueberries & whitepots, whitepot, bread and butter pudding \\ \hline \hline \end{tabular}
\end{table} 
Table 3: An example results of semantic aspect expansion. ## 4 Datasets and Evaluation Metrics

The cornerstone of evaluating this research lies in two key components: datasets and evaluation metrics. Datasets provide the ground truth labels against which a model's predictions are compared. Evaluation metrics, conversely, quantify the discrepancies between these predictions and the ground truth, enabling a rigorous assessment of the model's effectiveness. ### Datasets

SemEval (Semantic Evaluation) is an ongoing series of international workshops dedicated to assessing the effectiveness of research conducted by SIGLEX. SIGLEX, a special interest group within the Association for Computational Linguistics (ACL), aims to advance research in semantic analysis, interpreting meaning in language, and developing high-quality datasets for NLP. Each annual SemEval workshop defines topic-specific NLP and provides datasets for participants to use to develop and test systems [59]. SemEval's workshops in 2014, 2015, and 2016 are the focus of this research, which defines topic-specific NLP in ABSA [60; 61; 62]. SemEval of ABSA annotates labels with aspect categories, aspect terms (including single and multiple-word aspect terms), and polarities. The research community widely relies on SemEval datasets to assess and compare different algorithms and methodologies and gauge performance against the current state-of-the-art. This research utilizes the SemEval datasets from 2014, 2015, and 2016 to assess aspect terms within the restaurant domain. The statistics regarding the train and test datasets are provided in Table 4.

### Evaluation Metrics

The proposed model's performance in aspect-term extraction was assessed on the test datasets using standard metrics such as precision, recall, and F-measure. These metrics were calculated through Equations (5), (6), and (7), respectively, as outlined in reference [63]. \[Precision=\frac{TP}{TP+FP} \tag{5}\]

\[Recall=\frac{TP}{TP+FN} \tag{6}\]

\[F\text{-}measure=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{7}\]

In Equations (5) and (6), True Positives (\(TP\)) denote the count of instances correctly predicted as positive, meaning they were identified as aspect terms accurately. False Positives (\(FP\)) refer to the count of instances predicted as positive but were actually negative, signifying aspect terms that were identified incorrectly. False Negatives (\(FN\)) represent the count of positive instances predicted as negative, indicating aspect terms that were missed. In Equation (7), the \(F\)-_measure_ represents the harmonic mean of precision and recall, serving as a balanced measure of the model's performance. ## 5 Experimental Results

This section discusses the experimental results of aspect term extraction. The experiment was carried out on the SemEval datasets within the restaurant domain and assessed using standard metrics, as depicted in Table 5. Table 6 outlines the comparison of the