Patent classification is a fundamental step in the patent examination process, directly impacting the efficiency and quality of substantive review. Existing methods mostly focus on general texts like titles and abstracts, thus ignoring the key technical content claims and the corresponding citation relationships. Meanwhile, these approaches treat labels as independent targets, failing to exploit the semantic and structural information within the label taxonomy. To address these problems, we propose a Claim Structure based Patent Classification model with Label Awareness (CSPC-LA). The method first utilizes the citation relationship of patent claim texts to construct the citation graph and the co-reference graph. Then structural graph learning is used on both graphs to mine the internal logic of patent claims. Finally, we optimize the tree hierarchy of IPC labels and employ tree propagation learning to enhance the patent representation. Extensive experiments on the latest patent classification dataset from USPTO demonstrate that the proposed method is more effective than the state-of-the-art baselines.

The rise of multimodal content on social platforms has led to the rapid spread of complex and persuasive false narratives, combining of text and images. Traditional rumor detection models attempt to identify such content by relying on textual cues or employing shallow multimodal fusion techniques. However, these methods often assume a simplistic one-to-one alignment between modalities, overlooking the richer hierarchical relationships across modalities, failing to capture the layered structure of meaning. In this paper, we present RumorCone, a novel method that employs hyperbolic geometry in order to preserve hierarchical, non-linear relationships, rather than representing them at a flat semantic level. First, RumorCone decomposes image and text content into three levels: base, mid, and high-level abstractions, and embeds them in hyperbolic space to model their tree-like semantic structure. Second, a dynamic hyperbolic multimodal attention mechanism aligns features across modalities and levels, and a flexible fusion strategy adjusts the contribution of each modality based on alignment quality. Our experiments indicate the importance of hierarchical semantic modeling for robust and interpretable multimodal rumor detection.