Ambiguity-Aware Keyword-Enhanced Label-Aware Semantic Fusion for Text Classification

Abstract

The rapid growth of textual data has made text classification a fundamental task in Natural Language Processing (NLP). However, real-world texts often exhibit semantic ambiguity, limited contextual information, and unclear category boundaries, which hinder conventional models from learning discriminative representations. To address these challenges, this paper proposes an Ambiguity-Aware Semantic Fusion Framework (AAK-LASFNet) for robust text classification.The proposed model constructs dual-view semantic representations by combining local contextual features extracted by a TextRCNN encoder with global semantic knowledge obtained from a large language model. An ambiguity estimation module is introduced to model semantic uncertainty, improving the model’s ability to handle ambiguous samples. Meanwhile, a label-aware attention mechanism and a keyword enhancement module are employed to strengthen category-related semantic cues. To further capture complex interactions between local and global representations, a high-order semantic fusion strategy is developed. In addition, a semantic consistency loss is imposed to align different semantic views and enhance representation stability.Extensive experiments on four benchmark datasets demonstrate that the proposed framework consistently outperforms strong baselines in terms of Accuracy, highlighting its effectiveness in alleviating semantic ambiguity in text classification.