基于层叠条件随机场的事件因果关系抽取

摘要

传统的事件因果关系抽取方法只能覆盖文本中的部分显式因果关系.针对这种不足,提出一种基于层叠条件随机场模型的事件因果关系抽取方法.该方法将事件因果关系的抽取问题转化为对事件序列的标注问题,采用层叠(两层)条件随机场标注出事件之间的因果关系.第一层条件随机场模型用于标注事件在因果关系中的语义角色,标注结果传递给第二层条件随机场模型用于识别因果关系的边界.实验表明,本文方法不仅可以覆盖文本中的各类显式因果关系,并且均能取得较好的抽取效果,总体抽取效果的F1值达到85.3％.%Traditional methods for event causal relation extraction covered only part of the explicit causal relation in the text. A method for event causal relation extraction is presented based on Cascaded Conditional Random Fields. The method casts the problem of event causal relation extraction as the labeling of event sequence. The Cascaded (Dual-layer) Conditional Random Fields is employed to label the causal relation of event sequence. The first layer of the Cascaded Conditional Random Fields model is used to label the semantic role of causal relation of the events, and then the output of the first layer is passed to the second layer for labeling the boundaries of the event causal relation. Experimental results show that this method not only covers each class of explicit event causal relation in the text, but also achieves good performance and the F-Measure of the overall performance arrives at 85.3%.The multi - segment linear ( MSL) feature of the time series are collected, and a time series classification algorithm is proposed, which consists of derivative estimation function, linear segmentation method and DDHMM model (base on HMM). Firstly, the derivative estimation function and the linear segmentation method can be used together to detect the MSL feature. If they are matched, time series can be converted into observed sequence with a special structure. Next, the training observed sequences can be used to train DDHMM models. After training, the time series are classified through comparing the probability value of testing observed sequences generated by each model. The experimental results show that the proposed algorithm has a high accuracy when classifying the time series that match the MSL feature, and it has good performance in the classification on the UCI dataset and the actual projects.