# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 1 Introduction  
With the rapid advancement of internet technologies, the volume of news text information has increased exponentially, characterized by real-time dissemination and highly fragmented content. The automatic classification of large-scale text data has emerged as a prominent research direction [1]. Text classification, a fundamental component of natural language processing (NLP), focuses on mapping textual data to a set of predefined labels [2]. It has found wide applications in the field of sentiment classification, spam detection, and public opinion monitoring [3, 4, 5]. The primary task of text classification is to convert text into a vector representation that is recognizable to computers. The quality of text representation directly influences the final classification results [6]. Traditional text representation methods, such as One-Hot Encoding, overlook grammatical and semantic relationships between words, resulting in high dimensionality and sparse matrices. Static word embedding models like Word2 Vec are trained on a corpus to generate richer semantic representations by capturing the relationships between words within a contextual window [7]. However, Word2 Vec overlooks theimportance of part-of-speech (POS), word frequency, and category distribution information for classification results. Specifically, the model should focus more on category-specific terms--words that are concentrated in specific categories. To address this, we propose an enhanced word embedding method that weights Word2 Vec vectors Term Frequency-Document Frequency Category-Distribution Weight (TF-IDF-CDW) and integrates POS features to obtain more semantically enriched representation vectors. The TF-IDF-CDW weighting method is based Term Frequency-Document Frequency Category (TF-IDF) with the consideration of category distribution, allowing for higher weights to be assigned to category-specific terms. After converting the text into a computer-recognizable embedding vector, machine learning or deep learning models are needed to further learn the vector features and extract the deep semantic information within the text vector. Traditional machine learning methods perform adequately on simple tasks but are often insufficient for more complex scenarios. Deep learning approaches, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), have shown superior capabilities in this domain [8; 9; 10]. CNNs effectively capture local patterns through convolutional filters, while RNNs are well suited for modeling sequential dependencies. To address the issues of gradient vanishing and explosion in traditional RNNs, researchers often use more-effective models such as Long Short-Term Memory (LSTM) networks or Gated Recurrent Units (GRUs) for text classification tasks [11; 12]. To simultaneously capture local key features and global contextual dependencies, we propose a hybrid neural model based on a Gated Convolutional Neural Network (GCNN) and two-layer Bidirectional Long Short-Term Memory (BiLSTM). The primary contributions of this paper can be summarized as follows:  
1. An enhanced word embedding model based on pretrained Word2 Vec is proposed. By incorporating Category Distribution Weight (CDW) into the refinement of TF-IDF, higher weights are assigned to category-specific terms. The TF-IDF-CDW-weighted Word2 Vec embeddings, combined with POS features, allow the embedding vectors to emphasize key terms while minimizing the impact of noise. 2. A hybrid model is designed by combining a GCNN with two BiLSTM networks. The GCNN captures local semantic patterns through multiscale gated convolutions. The BiLSTM layer captures global contextual dependencies, and a residual connection mechanism is incorporated to preserve the integrity of semantic representations. Finally, an attention mechanism dynamically adjusts the weights of the dual channels, enhancing classification performance. 3.