摘要
针对TempEval-2010会议所提供中文语料中的时序关系识别任务,采用基于条件随机场的方法自动识别获得信号词,并融入跨事件理论,利用基于最大熵模型的分类算法对信号词与其他语言特征进行时序关系识别,同时使用约束传播的推理方法解决语料稀疏问题。实验结果表明,基于条件随机场的方法信号词自动识别准确率为69.21%,融入跨事件理论的时序关系识别准确率达到84.7%,表明所提方法可有效改善识别效果。
For the temporal relation recognition task in Chinese corpus provided by TempEval-2010 conference, this paper uses a method based on conditional random field to automatically identify and obtain the signal words. Fused with cross event theory, it uses classification algorithm of maximum entropy model for temporal relation identification of the words and other linguistic features. Meanwhile, it uses the inference method of constraint propagation to solve the problem of sparse corpus. Experimental results show that the automatic recognition accuracy of signal words by using the method based on conditional random field is 69.21% , and the recognition accuracy of the temporal relation by method fused with cross event theory is 84.7% , which means that the proposed method can improve the recognition effect.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第6期189-194,共6页
Computer Engineering
基金
国家自然科学基金"基于篇章特征的越南语新闻事件信息抽取关键技术研究"(61562049)
关键词
时序关系
信号词
条件随机场
最大熵
新闻事件
temporal relation
signal word
conditional random field
maximum entropy
news events
