TY - GEN
T1 - Constructing and embedding abstract event causality networks from text snippets
AU - Zhao, Sendong
AU - Wang, Quan
AU - Massung, Sean
AU - Qin, Bing
AU - Liu, Ting
AU - Wang, Bin
AU - Zhai, Cheng Xiang
N1 - We are grateful to Prof. Wangxiang Che, Jing Liu, Jiang Guo and the anonymous reviewers for their insightful comments and suggestions. This work was supported by the National Key Basic Research Program of China (973 Program) via grant 2014CB340503 and the National Natural Science Foundation of China (NSFC) via grants 61133012, 61472107 and 61402465. This work was done while the author was visiting Institute of Information Engineering Chinese Academy of Sciences and University of Illinois at Urbana-Champaign.
PY - 2017/2/2
Y1 - 2017/2/2
N2 - In this paper, we formally define the problem of representing and leveraging abstract event causality to power downstream applications. We propose a novel solution to this problem, which build an abstract causality network and embed the causality network into a continuous vector space. The abstract causality network is generalized from a specific one, with abstract event nodes represented by frequently cooccurring word pairs. To perform the embedding task, we design a dual cause-effect transition model. Therefore, the proposed method can obtain general, frequent, and simple causality patterns, meanwhile, simplify event matching. Given the causality network and the learned embeddings, our model can be applied to a wide range of applications such as event prediction, event clustering and stock market movement prediction. Experimental results demonstrate that 1) the abstract causality network is effective for discovering high-level causality rules behind specific causal events; 2) the embedding models perform better than state-of-the-art link prediction techniques in predicting events; and 3) the event causality embedding is an easy-to-use and sophisticated feature for downstream applications such as stock market movement prediction.
AB - In this paper, we formally define the problem of representing and leveraging abstract event causality to power downstream applications. We propose a novel solution to this problem, which build an abstract causality network and embed the causality network into a continuous vector space. The abstract causality network is generalized from a specific one, with abstract event nodes represented by frequently cooccurring word pairs. To perform the embedding task, we design a dual cause-effect transition model. Therefore, the proposed method can obtain general, frequent, and simple causality patterns, meanwhile, simplify event matching. Given the causality network and the learned embeddings, our model can be applied to a wide range of applications such as event prediction, event clustering and stock market movement prediction. Experimental results demonstrate that 1) the abstract causality network is effective for discovering high-level causality rules behind specific causal events; 2) the embedding models perform better than state-of-the-art link prediction techniques in predicting events; and 3) the event causality embedding is an easy-to-use and sophisticated feature for downstream applications such as stock market movement prediction.
KW - Causality
KW - Embedding methods
KW - Event causality network
KW - Event prediction
KW - Stock price movement prediction
UR - https://www.scopus.com/pages/publications/85015266606
UR - https://www.scopus.com/pages/publications/85015266606#tab=citedBy
U2 - 10.1145/3018661.3018707
DO - 10.1145/3018661.3018707
M3 - Conference contribution
AN - SCOPUS:85015266606
T3 - WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
SP - 335
EP - 344
BT - WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery
T2 - 10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Y2 - 6 February 2017 through 10 February 2017
ER -