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A Willing Events Identification Method

机译:愿意的事件识别方法

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Events extraction is an important part of information extraction, at present, most events extraction aim at extracting events themselves but do not consider whether the events have already happened. This paper focuses on the events named willing events which have a leady proposed and made sure to happen but not yet happened currently. There exist quite a number of willing events in many areas, and those can provide a lot of information, thus, carries on the extraction is very meaningful. This paper presents a new feature selection method based on Dependency Parsing. Dependency parsing is used to find the syntactic relations among the words expresses willingness, event denoters and other words. Then these features are used in machine learning to classify the events, and finally achieve the identification of willing events. The experiment shows that, aiming at willing events, the features found based on dependency parsing do better than those features used in traditional events identification.
机译:事件提取是信息提取的重要组成部分,目前大多数事件提取旨在提取事件本身,但不考虑事件是否已发生。本文侧重于名为愿意事件的事件,这些事件有一个引用的提议,并确保目前尚未发生。许多领域存在许多愿意的事件,而且可以提供大量信息,因此,对提取的携带非常有意义。本文介绍了一种基于依赖性解析的新特征选择方法。依赖解析用于找到词语中的语法关系,表达意愿,事件致辞和其他单词。然后在机器学习中使用这些功能来对事件进行分类,最后实现了愿意的识别事件。实验表明,针对愿意的事件,基于依赖性解析的特征比传统事件识别所使用的特征更好地做得好。

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