Causal explanations in political narratives are often framed and contested.Different sources may explain the same event by assigning responsibility to different actors, expressing different levels of certainty.Standard Event Causality Identification (ECI) focuses on detecting causal links and does not capture these distinctions.We introduce Framing-Aware Event Causality Identification (FrECI), a framing-aware extension of ECI that models causal explanations as structured claims including responsibility targets, evaluative framing, source type, and epistemic modality grounded in established framing theories.We construct a multilingual dataset aligned across English, Chinese, and Arabic narratives using shared event anchors.We evaluate FrECI using prompt-based large language model baselines and supervised neural models.Results show that prompt-based baselines struggle to recover complete framed causal claims, while joint supervised models perform substantially better.Finally, we demonstrate that FrECI enables quantitative analysis of divergent causal attribution across narratives.The dataset and code are publicly available.1

News articles often describe the same realworld event in strikingly different ways, shaping perception through framing rather than factual disagreement.However, traditional computational framing approaches often rely on coarse-grained topic classification, limiting their ability to capture subtle, event-level differences in how the same occurrences are presented across sources.We introduce Framingdivergent Event Coreference (FRECO), a novel task that identifies pairs of event mentions referring to the same underlying occurrence but differing in framing across documents to provide a event-centric lens for computational framing analysis.To support this task, we construct the high-agreement and diverse FRECO corpus.We evaluate the FRECO task on the corpus through supervised and preference-based tuning of large language models, providing strong baseline performance.To scale beyond the annotated data, we develop a bootstrapped mining pipeline that iteratively expands the training set with high-confidence FRECO pairs.Our approach enables scalable, interpretable analysis of how media frame the same events differently, offering a new lens for contrastive framing analysis at the event level.The dataset and code will be made publicly available.1

Jin Zhao, Jingxuan Tu, Bingyang Ye, Xinrui Hu, Nianwen Xue, James Pustejovsky. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

In this paper, the position/velocity tracking control problem of high speed train(HST) is investigated with considering some inevitable factors such as the input nonlinearity due to different notches of traction/braking forces, aerodynamic resistance, in-train force, external disturbance and unknown actuator failures, which lead to the uncertainty and nonlinearity of HST. Aiming at the system characteristics of HST, a set of integer-order control methods based on the excellent approximation ability of Radial Basis Function Neural Network(RBFNN) are established firstly, and motivated by them, a kind of RBFNN-based fractional-order control methods are proposed by utilizing the genetic attenuation properties of fractional calculus(FC) in order to improve the control performance in the work. It should be pointed out that all the developed methods are able to deal with uncertainties and nonlinearities as well as actuator failures without the need for any “trail and error” process. The feasibility and effectiveness of the proposed control methods are verified by Lyapunov theoretical analysis and numerical simulation studies. Besides, the control performance of integer-order control system and fractional-order control system is compared and analyzed, and the results show that the fractional-order control system is superior.