Abstract:

The identification of key elements in cases is a core task in intelligent analysis of judicial texts, and has significant value in scenarios such as case retrieval and judicial decision support. However, the “low-resource” nature of scarce labeled data in the judicial domain limits the performance of traditional named entity recognition methods that rely on large-scale labeled data. This paper proposed a recognition model that integrated label semantic information, embedding entity type labels as prompt information into the text encoding process. By constructing an interaction mechanism between label anchor vectors and contextual text vectors, the model explicitly captured the semantic associations between labels and text, enhancing its understanding of element type semantics and its ability to locate element boundaries in low-resource scenarios. Experimental results show that the proposed method outperforms baseline models on low-resource case datasets, demonstrating the enhancement effect of label semantics on key element identification and providing a new solution for low-resource information extraction tasks in the judicial domain.

Key words: identification of key case elements, low resources, label semantics, named entity recognition, judicial text analysis