Open-domain event detection aims to identify known event types while discovering previously unseen ones from unstructured documents. In practice, event detection systems often operate with an evolving event set rather than a complete type inventory. Under this setting, existing zero-shot event detection methods can fail in two ways when the event type is absent from the predefined set: the model may either miss the event or assign it to a plausible but wrong known type, hiding the need for discovery. To address this problem, we propose OGED, an  ̲Ontology- ̲Guided open-domain  ̲Event  ̲Detection framework for grounding–discovery resolution, i.e., deciding whether an event mention should be assigned to an existing type or should induce a new type. OGED organizes the available event types into a hierarchical search structure, uses cluster-level scheduler and adaptive planning to keep known-type assignment and new-type discovery jointly active, and applies retrieval-gated verification together with conflict selection to resolve competing assignments before output. Experiments on a unified benchmark with 564 event types show that OGED outperforms strong baselines on both known-type detection and unknown-type discovery. Code is available at https://github.com/chenzhouli/OGED.

“As an important means of disseminating Chinese cultural heritage, the development of Chinese handwriting skills faces dual challenges in the digital era: insufficient pedagogical resources anda lack of personalized feedback. At the 24th China National Conference on Computational Linguistics (CCL 2025), we organized a handwritten Chinese character evaluation task focusing on writing quality grading and comments generation. This benchmark utilized an expert-annotated calligraphic dataset to enhance task efficacy. Eight teams participated in the evaluation, three ofwhich submitted valid entries. In the character grading subtask, the top-performing team achieved an F1-score of 90.5%, whereas the optimal system in the comments generation subtask attained a score of 52.8%.”