【摘要】 提出了一种专项新闻语义框架以及借助该框架识别来自动检索、分类客户负面新闻的方法.与传统基于词的语义框架不同,负面新闻事件语义框架针对每个单一语境负面新闻事件子类来定义语义框架.通过构建框架知识库、领域词汇本体库及与框架关联的样本句子库,融合利用依存语法、词性标记技术和植入任务型代价的对数线性分类建模技术,实现了负面事件语义框架的自动识别.测试表明,该方法实用、高效,对知识库中已预定义的负面事件新闻类识别,在准确率、召回率和处理效率上都有很好的表现.更多还原

【Abstract】 A novel negative news retrieving semantic frame( NNFrame) and its identification were presented.Different from traditional semantic Frame Nets,which were defined based on word-sense disambiguation,NNFrames were defined on each subcategory of negative news with a single semantic context. By constructing the NNFrame knowledge base,domain ontology repository,and a collection of annotated example sentences for each NNFrame,a method was described for identifying NNFrame by a task-specific extended conditional log-likelihood model,that takes dependency-syntax structure representations,and the part of speech tags as input. This approach is practical,efficient,and can achieve state-of-the-art results on precision / recall metrics for identification and classification of negative news whose subcategories are pre-defined in the NNFrame knowledge base.更多还原