Data is published on the web over time in great volumes, but majority of the data is unstructured, making it hard to understand and difficult to interpret. Information Extraction (IE) methods obtain structured information from unstructured data. One of the challenging IE tasks is Event Extraction (EE) which seeks to derive information about specific incidents and their actors from the text. EE is useful in many domains such as building a knowledge base, information retrieval and summarization. In the past decades, some event ontologies like ACE, CAMEO and ICEWS were developed to define event forms, actors and dimensions of events observed in the text. These event ontologies still have some shortcomings such as covering only a few topics like political events, having inflexible structure in defining argument roles and insufficient gold-standard data. To address these concerns, we propose an event ontology, namely COfEE, that incorporates both expert domain knowledge and a data-driven approach for identifying events from text. COfEE consists of two hierarchy levels (event types and event sub-types) that include new categories relating to environmental issues, cyberspace and criminal activity which need to be monitored instantly. Also, dynamic roles according to each event sub-type are defined to capture various dimensions of events. In a follow-up experiment, the proposed ontology is evaluated on Wikipedia events, and it is shown to be general and comprehensive. Moreover, in order to facilitate the preparation of gold-standard data for event extraction, a language-independent online tool is presented based on COfEE. A gold-standard dataset annotated by 10 human experts is also prepared consisting 24K news articles in Persian language. Finally, we present a supervised method based on deep learning techniques to automatically extract relevant events and corresponding actors.
翻译:长期在网上公布大量数据,但大多数数据没有结构化,难以理解和解释。信息提取(IE)方法从非结构化数据中获取结构化信息。一个具有挑战性的IE任务是“Expleton”(EE),它试图从文本中获取具体事件及其行为者的信息。EE在许多领域非常有用,如建立知识库、信息检索和汇总。在过去几十年,一些事件,如ACE、CAMEO和ICEWS等,是用来界定在文本中观察到的事件形式、行为者和层面的。信息提取(IE)方法从非结构化数据获得结构化信息。信息提取(IEE)方法仍然有一些缺陷,例如只涵盖几个主题,如政治事件(Eproduct Exprilon)信息。“Explicipleononon(EEEEEEEE)方案包括专业领域知识、信息检索和数据驱动程序。“CofEEEE”方案包括两种等级(事实类型和事件次类型),在与环境问题相关的新类别、网络空间空间和犯罪统计活动中提供数据,最后活动。根据动态活动来对数据进行评估。