Z. Liu

Yao Liu, Tien-Ping Tan, Zhilan Liu

Chinese Legal Case Retrieval (LCR) benchmarks grade a reference judgment relevant when its legal characterization matches the query, and strong systems now reach NDCG@10 of 0.85-0.88. Most of the BM25-to-best-trained gap is recoverable with no retrieval model: ranking candidates only by shared primary charge, broken by BM25, closes 99.2% of it on LeCaRDv2 -- with no detectable difference from the best-trained system. This reflects benchmark design: LeCaRDv2 defines top relevance via the crime's key constitutive elements, which encode the charge, so same-charge cases are relevant by construction (relevance lift 4.49; charge-to-relevance macro-AUC 0.871). Holding charge fixed, the trained reranker's advantage over BM25 collapses to a small within-charge residual (+0.026 NDCG@10, cluster-bootstrap CI excluding zero, about a quarter), the only non-definitional positive. The effect is not uniform: the same rule recovers 84.3% on LeCaRDv1 and is out of spec on CAIL2022, with the charge-to-relevance signal weakening in step (macro-AUC 0.871/0.759/0.728); a predicted-charge cascade reproduces 76.6% on LeCaRDv2 but does not transfer. The construct is also cashable at first stage: an exploratory zero-training charge-pool channel lifts LeCaRDv2 recall (R@100 +0.025, wrong-charge controls hurt), reported as a positive control for the confound, not a retrieval method or novelty claim. Charge is thus a high-leverage construct-validity factor at the benchmark level -- not auniform explanation of NDCG@10, and not evidence that any system relies on charge. We package established construct-validity and partial-input checks as a reusable charge-controlled protocol (CCE); on all three benchmarks its triggers come back null or descriptive, behaving as designed. We release the scripts, schema, and protocol so future benchmarks can be screened before their NDCG@10 is read as legal-reasoning ability.

Yao Liu, Zhilan Liu, Tien Ping Tan et al.

Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection.