一种非监督的事件触发词检测和分类方法

事件触发词检测和分类是事件抽取中至关重要的第一步。传统的抽取和分类方法往往倾向于监督学习方法，如条件随机场、SVM等，但由于这类方法需要繁重的人工标注且受限于预先定义好的类别，因此很难在开放领域中得到应用。提出了一种非监督的事件触发词检测和分类方法，利用主题模型获取候选触发词在主题上的分布，然后利用二值状态自动机模型捕获高概率的主题，从而筛选出真正的事件触发词和相应的分类。在大规模的未标注新浪新闻数据集上的实验结果充分验证了本文方法的有效性。

Unsupervised method for event trigger identification and classification

The identification and classification of event trigger plays a decisive role in event extraction. Usually, the trigger words are extracted based on supervised machine learning methods such as CRF. However, since these methods rely on expensive manual annotation and require predefined event types, they are not sufficient for open domain application. In this paper, we present an unsupervised method for event trigger identification and classification. First, we run a topic model to obtain the topic distribution over each candidate trigger word. Then, an improved two state automaton is proposed to detect the real trigger word and capture the corresponding topics. The experiment on a large unlabeled corpus shows our unsupervised model is very inspiring.