一种基于事件本体的文本事件要素提取方法

在事件信息的抽取中,事件要素的提取是一个难点。现有的事件要素抽取主要是基于机器学习的方法,这类方法容易受到语料稀疏性的影响。该文提出一种基于事件本体的事件要素提取方法,该方法将事件要素推理分为两步: 一、通过事件要素词和事件指示词的位置关系来初步填充要素值,并将得出的置信度较高的事件作为种子事件;二、利用第一步得出的种子事件,查询事件本体中的事件类约束和基于事件非分类关系的推理规则,并对要素进行推理,进一步对事件要素进行填充和修正。实验结果表明,该方法能较好地提升事件要素提取的准确度。

A Text Event Elements Extraction Method Based on Event Ontology

Extraction of event elements is a challenge in event-based information extraction. Currently, the main solutions are based on machine learning method which is subject to the corpus sparsity. This paper proposes an event element extraction method based on event ontology. Event elements reasoning process includes two steps: Firstly, elements values are initially complemented according to positional relations between event elements words and event indicators words, selecting the event with the highest confidence as the seed event; Secondly, search the seed events to for their event classes restrictions and non-taxonomic relations from event ontology, to complement and revise event elements. The experimental results show that this method can improve the accuracy of event elements extraction.