Discovering Concept-Level Event Associations from a Text Stream

被引：2

作者：

Ge, Tao ^{[1
,2
]}

Cui, Lei ^{[3
]}

Ji, Heng ^{[4
]}

Chang, Baobao ^{[1
,2
]}

Sui, Zhifang ^{[1
,2
]}

机构：

[1] Peking Univ, Key Lab Computat Linguist, Minist Educ, Sch EECS, Beijing, Peoples R China

[2] Collaborat Innovat Ctr Language Abil, Xuzhou, Peoples R China

[3] Microsoft Res, Beijing, Peoples R China

[4] Rensselaer Polytech Inst, Troy, NY USA

来源：

NATURAL LANGUAGE UNDERSTANDING AND INTELLIGENT APPLICATIONS (NLPCC 2016) | 2016年 / 10102卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1007/978-3-319-50496-4_34

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We study an open text mining problem - discovering concept-level event associations from a text stream. We investigate the importance and challenge of this task and propose a novel solution by using event sequential patterns. The proposed approach can discover important event associations implicitly expressed. The discovered event associations are general and useful as knowledge for applications such as event prediction.

引用

页码：413 / 424

页数：12

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