BiLSTM-enhanced legal text extraction model using fuzzy logic and metaphor recognition.

ABSTRACT: The burgeoning field of natural language processing (NLP) has witnessed exponential growth, captivating researchers due to its diverse practical applications across industries. However, the intricate nature of legal texts poses unique challenges for conventional text extraction methods. To surmount these challenges, this article introduces a pioneering legal text extraction model rooted in fuzzy language processing and metaphor recognition, tailored for the domain of online environment governance. Central to this model is the utilization of a bidirectional long short-term memory (Bi-LSTM) network, adept at delineating illicit behaviors by establishing connections between legal provisions and judgments. Additionally, a self-attention module is integrated into the Bi-LSTM architecture, augme

SUBMITTER: Chen J 

PROVIDER: S-EPMC11888925 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature