基于框架语义扩展训练集的有监督事件检测方法

事件检测是信息抽取领域的一个重要研究方向,目前的事件检测方法往往受限于数据稀疏、语料例句分布不平衡和歧义问题。该文研究发现框架语义知识库FrameNet(FN)含有丰富的已标注框架的语料,并且FN中定义的框架和事件检测中定义的事件具有极其相似的结构。框架由词法单元和一组框架元素组成,可与事件中的触发词和论元形成对应关系;而且,FN中的许多框架实际上也能表达某些事件。因此,该文利用这一相似性构建事件类型与框架类型的映射关系,从而选取FN中合适的例句作为事件检测的扩充语料,以此来优化事件检测性能。实验结果显示,针对触发词识别任务和事件类型识别任务,该文提出的框架语义辅助方法取得了较好的效果。

Frame Semantics Based Training Data Expansion for Supervised Event Detecting

Event detection is an important research issue in the field of information extraction. The current methods of event detection generally suffer from data sparseness, imbalanced distribution and ambiguity. This paper proposes to construct the correspondence between event types and frames in FrameNet (FN), so as to get additional samples to train the supervised detection models. It is revealed that FN consists of richer examples of events which have been annotated with the tags of frame semantics. In addition, the frame defined in FN shares high similarity with that in ACE: e. g. the lexical units and a set of frame elements inherently correspond to the event triggers and arguments in the ACE corpus, and many frames in FN can represent certain types of events. Experimental results show that the proposed method performs well both in trigger identification and event type recognition.