For the extraction of judicial events for Tibetan, a low-resource language, traditional simple neural network approaches struggle to adequately capture the deep semantics and features of the texts because Tibetan texts are usually lengthy and contain numerous judicial-related entities. To overcome this limitation, this research presents an event extraction model combining deep word representation with hybrid neural networks for the Tibetan judicial domain. The model introduces the Chinese minority pre-trained language model (CINO), which generates dynamic word vector representations, addressing the challenge of modeling the deep semantics inherent in Tibetan texts. During feature extraction, a bidirectional long short-term memory network (BiLSTM) is applied to extract the temporal and contextual dependencies, while a convolutional neural network (CNN) is utilized to capture the local semantic features to construct a comprehensive global semantic representation. Finally, the sequences are decoded through conditional random field (CRF) to generate optimal prediction results, thus achieving the efficient extraction of Tibetan judicial events. The experimental findings indicate that the model outperforms the baselines by achieving F1 scores of 70.47% for trigger detection and 62.99% for argument recognition, with improvements of 16.6% and 16.42%, respectively. These results confirm the effectiveness and superiority of the proposed model.

One new coumarin, one new flavonoid, two new distinctive compounds with characteristics of an iriflophene unit and a flavonoid unit connecting via a furan ring, together with five known compounds were isolated from the rhizomes of Phedimus aizoon (L.) ‘t Hart. Their structures were elucidated on the basis of spectroscopic analysis. Compounds 4, 5 and 8 increased the survival rate of H9c2 cells induced by doxorubicin in vitro, which was capable of further drug exploration. Graphical Abstract

Automated compliance checking for building fire protection design requires accurate extraction of domain-specific entities from technical code provisions. However, building fire protection codes contain highly specialized terminology, hierarchical clause structures, and complex regulatory semantics, which make manual information extraction time-consuming and error-prone. This study develops a domain-specific named entity recognition approach for Chinese building fire protection design codes. An expert-annotated dataset was constructed from five representative codes, containing 2748 annotated provisions and 13,877 entity mentions across nine entity categories. A RoBERTa-BiLSTM-CRF model was then developed to capture contextual semantic representations, bidirectional sequence dependencies, and label-transition constraints. Results suggest that the proposed model achieves 88.34%, 88.31%, and 88.32% for precision, recall, and F1-score, respectively. The extracted entities can be organized into structured and traceable records, providing a foundation for downstream building fire protection knowledge management and automated compliance checking.

This paper proposes a Tibetan judicial event argument extraction method based on machine reading comprehension (MRC) to address the challenges of data scarcity and insufficient model generalization in low-resource language scenarios. Unlike traditional methods, this work models event argument extraction as an MRC task, progressively identifying and extracting various event arguments through a question-guided approach. First, a strategy for constructing event knowledge-enhanced questions tailored to the Tibetan judicial domain is designed. Specifically, interrogative words are formulated for different types of event arguments, and event semantic information is incorporated into questions to effectively disambiguate questions. Second, a deep semantic understanding architecture for Tibetan judicial events based on the CINO (Chinese Minority Pretrained Language Model) is proposed, incorporating a multi-head self-attention mechanism to enhance semantic alignment and global understanding between event sentences and questions. Finally, a two-stage training strategy is proposed for low-resource languages. Training is performed on a general Tibetan machine reading comprehension dataset, followed by task-adaptive fine-tuning on judicial domain data, effectively alleviating the data scarcity issue. Experimental results show that the proposed method achieved an F1-score of 76.59% in the Tibetan judicial event argument extraction task. This research offers new ideas for low-resource language event extraction and is of great significance for promoting intelligent information processing of minority languages.