语义增强的多策略政策术语抽取系统

Semantic Enhanced Multi-strategy Policy Term Extraction System

针对政策术语具有时效性、低频度、稀疏性和复合短语的特点,传统术语抽取方法难以满足需求的问题,设计实现了语义增强的多策略政策术语抽取系统.该系统从频繁项挖掘和语义相似度两个维度对政策文本特征进行建模,融合多种频繁模式挖掘策略选取特征种子词,利用预训练语言模型增强语义匹配来召回低频且稀疏的政策术语,实现了从无词库冷启动到有词库热启动半自动化的政策术语抽取.该系统能够提升政策文本分析效果,为建设智慧政务服务平台提供技术支持.

Policy terms are characterized by timeliness, low frequency, sparsity, and compound phrases. To address the difficulty of traditional term extraction methods in meeting demands, we design and implement a semantic enhanced multi-strategy system of policy term extraction. The system models the features of policy texts from the two dimensions of frequent item mining and semantic similarity. Feature seed words are selected by integrating multiple frequent pattern mining strategies. Low-frequency and sparse policy terms are recalled by pre-training the language model and enhancing semantic matching. Transforming from a cold start without a thesaurus to a hot start with a thesaurus, the system achieves semi-automatic extraction of policy terms. The proposed system can improve the effect of policy text analysis and provide technical support for the construction of a smart government service platform.