# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 2 Related Work
### Text Classification  
The evolution of text classification algorithms highlights a clear paradigm shift from shallow models to deep learning approaches. Conventional machine learning approaches depend on manually engineered features and statistical representations, which are subsequently fed into classifiers [24]. Although these methods performed well in early applications, their ability to handle high-dimensional, sparse, and semantically complex data has proven to be increasingly limited with the advent of large-scale data. In response, deep learning has since emerged as the dominant paradigm for text classification, offering end-to-end hierarchical semantic modeling capabilities [25]. [PERSON] et al. [26] designed the Deep Pyramid Convolutional Neural Network (DPCNN), which introduces a novel deep pyramid structure to expand the receptive field through equal-length convolutions and residual connections, improving semantic abstraction while maintaining computational efficiency. [PERSON] et al. [27] developed an adaptive convolutional model incorporating label embeddings to dynamically generate convolutional filters aligned with semantic labels, enhancing feature alignment and representation. [PERSON] et al. [28] proposed a convolutional framework that encodes text into three-dimensional matrices and employs dual convolutional filters to capture semantic information at both intra-sentence and inter-sentence levels, achieving superior performance over other CNN models on five benchmark datasets. While CNNs excel at capturing local patterns, they are limited in modeling sequential dependencies and contextual semantics, which are better handled by recurrent models such as LSTM and GRUs. To further improve sequential modeling, [PERSON] et al. [29] proposed an SHO-LSTM model for multi-label classification, employing a Spotted Hyena Optimizer to tune LSTM weights for better convergence. [PERSON] et al. [30] designed a memristor-based hardware acceleration solution for LSTM networks, \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Description** & **Main Advantages** \\ \hline Word2 Vec [7] & Generates low-dimensional, dense word vectors based on contextual window information. & Embedding vectors provide rich semantic representations. \\ \hline BERT [16] & Generates context-sensitive word vectors using a deep bidirectional Transformer network architecture. & Dynamically adapts embeddings based on context. \\ \hline Context-to-Vec [18] & Enhances Word2 Vec embeddings by integrating contextual information and refining it through graph-based topology, while improving semantic representations with synonym knowledge. & Addresses the limitations of static embeddings in semantic representation and context handling, significantly enhancing semantic expressiveness. \\ \hline SGCSTC [19] & Fuses multiple granularities of information, such as word-jieba, word-jieba-radical, word-ngram, word-ngram-radical, and character embeddings. & Enriches the semantic representation of the text and alleviates the context sparsity issue in short texts. \\ \hline LDA2 Vec [21] & Combines Word2 Vec with topic models to optimize domain-specific semantic representations through topic probabilities. & Improves word embedding by combining the strengths of LDA and Word2 Vec. \\ \hline Word2 Vec-TFIDF [22] & Calculates the importance of each word using TF-IDF, then adjusts Word2 Vec embeddings by applying these weights. & Retains the powerful semantic representation capability of Word2 Vec while enhancing the distinguishability of words in specific contexts. \\ \hline This paper & Quantifies the category distinction of words by using the improved TF-IDF-CDW weighting, dynamically adjusting Word2 Vec embeddings, and incorporating part-of-speech encoding. & Enhances the semantic weight of key terms by considering category distribution and part-of-speech and assigning higher weights to category-specific words. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of text representation methods.