Acquiring Event Relation Knowledge by Learning Cooccurrence Patterns and Fertilizing Cooccurrence Samples with Verbal Nouns
Acquiring Event Relation Knowledge by Learning Cooccurrence Patterns and Fertilizing Cooccurrence Samples with Verbal Nouns
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发表时间：
2008
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通讯作者：
Shuya Abe;Kentaro Inui;Yuji Matsumoto
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作者：
Shuya Abe;Kentaro Inui;Yuji Matsumoto
为了从大型语料库中获取事件之间的语义关系，本文提出了对最初为实体关系提取而设计的最先进方法的几种扩展，报告了我们在日语网络语料库上的实验结果。结果表明，(a)确实存在对事件关系获取有用的特定共现模式，(b)使用涉及动词名词的共现样本对召回率和精确度都有积极影响，(c)从 5 亿句子的网络语料库中获取了超过 5000 个关系实例，动作效果关系的精确度约为 66%。
Aiming at acquiring semantic relations between events from a large corpus, this paper proposes several extensions to a state-of-theart method originally designed for entity relation extraction, reporting on the present results of our experiments on a Japanese Web corpus. The results show that (a) there are indeed specific cooccurrence patterns useful for event relation acquisition, (b) the use of cooccurrence samples involving verbal nouns has positive impacts on both recall and precision, and (c) over five thousand relation instances are acquired from a 500M-sentence Web corpus with a precision of about 66% for action-effect relations.