"The International Classification of Diseases (ICD) provides a standardized framework for encoding diagnoses, serving critical roles in clinical scenarios. Automatic ICD coding aims to assign formalized diagnostic codes to medical records for documentation and analysis, which is challenged by an extremely large and imbalanced label space, noisy and heterogeneous clinical text,and the need for interpretability. In this paper, we propose a structured multi-class classification framework that partitions diseases into clinically coherent groups, enabling group-specific dataaugmentation and supervision. Our method combines input compression with generative and discriminative fine-tuning strategies tailored to primary and secondary diagnoses, respectively.On the CCL2025-Eval Task 8 benchmark for Chinese electronic medical records, our approach ranked first in the final evaluation."

Document-level Event Causality Identification (DECI) aims to extract causal relations between events in a document. It challenges conventional sentence-level task (SECI) with difficult long-text understanding. In this paper, we propose a novel DECI model (SENDIR) for better document-level reasoning. Different from existing works that build an event graph via linguistic tools, SENDIR does not require any prior knowledge. The basic idea is to discriminate event pairs in the same sentence or span multiple sentences by assuming their different information density: 1) low density in the document suggests sparse attention to skip irrelevant information. Our module 1 designs various types of attention for event representation learning to capture long-distance dependence. 2) High density in a sentence makes SECI relatively easy. Module 2 uses different weights to highlight the roles and contributions of intra- and inter-sentential reasoning, which introduces supportive event pairs for joint modeling. Extensive experiments demonstrate great improvements in SENDIR and the effectiveness of various sparse attention for document-level representations. Codes will be released later.