Enhancing Polyphonic Character Disambiguation in Chinese Text Processing with Bidirectional LSTM Networks

This study addresses the challenge of polyphonic character ambiguity in Chinese text processing through the development of a deep learning model capable of automatically identifying and resolving such ambiguities. The research begins with data collection and preprocessing, involving the acquisition of polyphonic character text data and a Chinese polyphonic character library, transformed into a suitable format for model input. Subsequently, a model architecture based on a bidirectional Long Short-Term Memory (LSTM) network was designed to enhance semantic understanding using 300-dimensional word embedding vectors. While achieving 82.98% accuracy on the test set, the model encountered high loss values during training, indicating potential issues like gradient vanishing. Therefore, future efforts will concentrate on refining the model structure and training methodology to enhance numerical stability and generalization capabilities, aiming for improved performance in real-world polyphonic character disambiguation tasks.