# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 2 Related Work  
### Text Representation  
Text representation is a fundamental step in text classification, aiming to transform unstructured textual data into machine-interpretable semantic vectors. Research in this area has evolved from traditional statistical and rule-based approaches to advanced pretrainedlanguage models. While conventional methods are computationally efficient, they often fail to capture syntactic rules and deep semantic relationships. In contrast, pretrained models built on large-scale text corpora yield richer context-aware features that strengthen model generalization. Depending on whether the embeddings change with context, word embeddings are typically categorized into static and dynamic variants [13]. [PERSON] et al. [7] introduced Word2 Vec, an influential static embedding model comprising two learning schemes: Bag-of-Words (CBOW), which infers a word from its context, and Skip-gram, which does the reverse. However, both are limited to local windows and cannot capture global co-occurrence features. To address this limitation, [PERSON] et al. [14] proposed GloVe, which incorporates global co-occurrence statistics into the embedding process to enhance semantic representation. Since static embeddings map words to fixed vectors regardless of context, they can sometimes lead to ambiguity. [PERSON] et al. [15] proposed ELMo, which uses a two-layer BiLSTM trained with forward and backward language models to generate context-sensitive word representations. Although ELMo improves performance on various NLP tasks, its shallow output fusion constrains deeper contextual interaction. [PERSON] et al. [16] later introduced BERT, based on bidirectional Transformer encoders, enabling simultaneous modeling of left and right contexts. BERT adopted a pretraining-fine-tuning paradigm and attained top results on various NLP benchmarks. Building on this, [PERSON] [17] proposed a dynamic BERT-based fusion framework that integrates BERT's global semantics with graph-based local topological features through a seven-stage heterogeneous interaction process. This method achieves notable improvements in text classification by combining structural awareness and context modeling. Despite the advantages of dynamic embeddings, static embeddings remain valuable in real-world applications due to their lower computational cost and faster inference. Numerous strategies have recently been explored to improve static vector representations. [PERSON] et al. [18] developed the Context-to-Vec framework, which infuses contextual information into static vectors and refines them using graph topology and synonym knowledge. [PERSON] et al. [19] proposed a six-granularity modeling approach for Chinese characters to enrich semantic information in Word2 Vec vectors. [PERSON] et al. [20] integrated word, character, and N-gram embeddings to improve fault classification in the aviation domain using CNN. [PERSON] et al. [21] introduced LDA2 Vec, which uses Word2 Vec to model topic-context relations and adjusts embeddings via topic probabilities to better reflect domain semantics. Empirical evidence supports the effectiveness of such enhancements. For instance, [PERSON] et al. [22] designed LogUAD, an anomaly detection model that utilizes TF-IDF-weighted Word2 Vec embeddings and achieved a significant 67.25% F1-score improvement over LogCluster. [PERSON] et al. [23] showed that combining Word2 Vec with TF-IDF outperformed both standalone Word2 Vec and Doc2 Vec in sentiment classification tasks. It is worth noting that traditional TF-IDF does not account for the distribution of words across categories, resulting in lower weights being assigned to words that frequently appear in specific categories. To address this limitation, we first introduced the concept of CDW and combined it with TF-IDF to form a new weighting calculation method, TF-IDF-CDW. We then used TF-IDF-CDW to weight Word2 Vec vectors and concatenate POS features to enhance the word embedding vectors.