TitleDiscovering Concept-Level Event Associations from a Text Stream
AuthorsGe, Tao
Cui, Lei
Ji, Heng
Chang, Baobao
Sui, Zhifang
AffiliationPeking Univ, Key Lab Computat Linguist, Minist Educ, Sch EECS, Beijing, Peoples R China.
Collaborat Innovat Ctr Language Abil, Xuzhou, Peoples R China.
Microsoft Res, Beijing, Peoples R China.
Rensselaer Polytech Inst, Troy, NY USA.
Peking Univ, Key Lab Computat Linguist, Minist Educ, Sch EECS, Beijing, Peoples R China.
Sui, ZF (reprint author), Collaborat Innovat Ctr Language Abil, Xuzhou, Peoples R China.
Issue Date2016
Publisher5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL)
Citation5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL).2016,10102,413-424.
AbstractWe 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.
URIhttp://hdl.handle.net/20.500.11897/470118
ISSN0302-9743
DOI10.1007/978-3-319-50496-4_34
IndexedCPCI-S(ISTP)
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室

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