多种语义特征在突发事件新闻中的共指消解研究

提高突发事件应对的关键在于快速地收集和提取相关新闻报道中的有用信息,共指消解是信息提取研究的重要子任务。该文采用最大熵模型对汉语突发事件新闻报道中的共指现象进行消解,综合对比了语义类特征、语义角色特征,以及基于维基百科的语义相关特征,重定向特征及上下文特征在测试集上的效果。实验结果表明,除单纯使用语义角色特征会使系统F值下降1.31％以外,其余各种语义知识对共指消解模型的结果均有所提高。

Multiple Semantic Features Based Coreference Resolution in Emergency News

The key to improving the emergency response ability lies in collecting and extracting the useful information from the relevant news reports effectively. The coreference resolution is an important subtask for this purpose. In the paper, we present an approach to coreference resolution based on Maximum Entropy Model for Chinese news reports about sudden events. We exploit the semantic class features, the semantic role features, as well as the semantic related features, the redirection features and context features extracted from the Wikipedia. The experimental results confirm the positive effect to the coreference resolution by adding selected semantic knowledge except pure semantic role which can make the system F value dropped by 1.31%.