# Tibetan Judicial Event Extraction Based on Deep Word Representation and Hybrid Neural Networks
###### Abstract  
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. l 202515332  
1  
Academic Editor: [PERSON]  
1  
Academic Editor: [PERSON]  
and whether the suspect surrendered all serve as key elements in determining the conviction and sentence. However, legal texts are often presented in an unstructured form, and when faced with a large amount of complex textual contents, judges may overlook key event elements, which may adversely affect the judgment of the case. Relying solely on the manual extraction of event elements is not only inefficient but also consumes a lot of labor and time. Judicial event extraction aims to automatically identify event-centered multidimensional event elements in judicial cases, assisting judicial workers to quickly reconstruct the facts of the case and clarify the focus of the dispute [13; 14; 15]. Therefore, how to effectively mine the rich event information contained in judicial data has become one of the core issues in the study of intelligent justice. Currently, research on judicial event extraction primarily targets high-resource languages like Chinese and English, and the research on Tibetan is still in its infancy, with relatively limited data and research results [11; 16; 17]. The study of judicial event extraction in Tibetan not only helps to improve the efficiency of judicial work but also has great significance in promoting national unity and social stability and maintaining national security. Tibetan has unique ordering rules that make Tibetan judicial event extraction have its own unique difficulties [18]. Table 1 shows a comparison between Tibetan and Chinese-English ordering rules. Chinese and English typically follow a subject-verb-object (SVO) structure, whereas Tibetan, as a language with a verb-final order, generally follows a subject-object-verb (SOV) structure. The predicate verb in Tibetan serves as the syntactic core and is crucial for conveying the sentence's meaning [19]. In event extraction tasks, identifying the predicate verb is the first step, as it functions as the event trigger [20]. However, when dealing with lengthy Tibetan judicial texts, the predicate verb is far away from each key piece of information, making it challenging to capture the dependencies between them. In addition, Tibetan judicial event extraction involves many event elements. Figure 1 shows an event fragment about \"Theft\", which contains several event elements, such as \"Thief\", \"Victim\", \"Amount\", \"Time\", and \"Place\".