TitleSeRI: A Dataset for Sub-event Relation Inference from an Encyclopedia
AuthorsGe, Tao
Cui, Lei
Chang, Baobao
Sui, Zhifang
Wei, Furu
Zhou, Ming
AffiliationSchool of EECS, Peking University, Beijing, China
Microsoft Research Asia, Beijing, China
Issue Date2018
Publisher7th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2018
Citation7th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2018. 2018, 11109 LNAI, 268-277.
AbstractMining sub-event relations of major events is an important research problem, which is useful for building event taxonomy, event knowledge base construction, and natural language understanding. To advance the study of this problem, this paper presents a novel dataset called SeRI (Sub-event Relation Inference). SeRI includes 3,917 event articles from English Wikipedia and the annotations of their sub-events. It can be used for training or evaluating a model that mines sub-event relation from encyclopedia-style texts. Based on this dataset, we formally define the task of sub-event relation inference from an encyclopedia, propose an experimental setting and evaluation metrics and evaluate some baseline approaches�?performance on this dataset. © Springer Nature Switzerland AG 2018.
URIhttp://hdl.handle.net/20.500.11897/530636
ISSN9783319995007
DOI10.1007/978-3-319-99501-4_23
IndexedEI
Appears in Collections:信息科学技术学院

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