## 3 Results
### Datasets  
Our experiments covered five widely used public datasets, including Yelp Review Polarity, AG's News, Yelp Review Full, and DBpedia. By validating our model on these public datasets, we ensure the reliability and reproducibility of the results. These datasets also provide a relative standard benchmark for evaluating and comparing the performance of different models on specific tasks. Additionally, other researchers can easily access the same datasets for validation purposes. Table 1 presents the basic characteristics of the datasets. Yelp Review Polarity: The Yelp Review dataset consists of reviews from Yelp. It was curated by [PERSON] et al. [9] in 2015 from the Yelp Dataset Challenge. This dataset is a binary sentiment classification dataset, where the reviews are categorized as either positive or negative sentiment. AG's News: This dataset originates from ComeToMyHead, an academic news search engine, and it consists of news articles from over 2000 news sources. The dataset was created by [PERSON] et al. [9] in 2015 from these news articles and contains 120,000 training samples and 7600 testing samples. Each sample is a short text categorized into one of four classes: World, Sports, Business, and Sci/Tech. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Classes** & **Training Set** & **Test Set** \\ \hline Yelp Review Polarity & 2 & 560,000 & 38,000 \\ \hline AG’s News & 4 & 120,000 & 7600 \\ \hline Yelp Review Full & 5 & 650,000 & 50,000 \\ \hline DBPedia & 14 & 560,000 & 70,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of all used experimental datasets. Yelp Review Full: This dataset shares the same origin as the Yelp Review Polarity dataset and was also constructed by [PERSON] et al. [9]. The Yelp Review Full dataset is a multi-class dataset consisting of 5 categories. In contrast to the Yelp Review Polarity dataset, Yelp Review Full provides a more fine-grained sentiment rating ranging from 1 to 5. DBpedia: The DBpedia ontology classification dataset was constructed by [PERSON] et al. [9] based on DBpedia 2014. It is an English ontology classification dataset. The dataset consists of 14 different categories, including Company, EducationalInstitution, Artist, Athlete, OfficeHolder, MeanOfTransportation, Building, NaturalPlace, Village, Animal, Plant, Album, Film, and WrittenWork. These datasets cover different domains and sources, aiming to thoroughly evaluate the performance of our model in diverse scenarios. These datasets have diverse characteristics, including different data sizes and numbers of categories, providing us with a wide testing environment. Firstly, these datasets have varying data sizes, ranging from small-scale datasets to large-scale datasets. Small-scale datasets help us quickly validate the effectiveness and stability of the model, while large-scale datasets validate the model's generalization ability and test its scalability in handling large-scale problems. Secondly, these datasets cover different numbers of categories. Some datasets involve binary classification tasks, while others involve multi-class classification problems. Such diversity allows us to test the performance of the model in handling different categories and label quantities, helping us comprehensively evaluate the model's ability for diverse classification tasks. By conducting experiments and comparisons on these datasets, we can gain in-depth understanding and evaluation of the applicability and advantages of our proposed model in various scenarios. Prior to conducting experiments, we performed data preprocessing on the raw datasets to provide clean, standardized, and manageable data, laying the foundation for subsequent experiments and training. After undergoing steps such as data cleaning and tokenization, the raw dataset can be used to construct processed training and testing sets. One crucial step is building the vocabulary. Since our model does not utilize pre-trained parameters, we need to train the model based on the constructed vocabulary.