Abstract
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.
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We treat co-burst as a special case of BSPs.
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Here, a named entity is considered as a unigram even if it is composed of multiple words such as Hong Kong.
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Cosine similarity computed based on word embeddings trained on English Gigaword corpus.
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The annotators mainly used ConceptNet and Wikipedia as references to help with the annotation.
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Acknowledgements
We appreciate the helpful comments of the reviewers. This work is supported by the National Key Basic Research Program of China (No. 2014CB340504), the Research Fund for the Doctoral Program of Higher Education (20130001110027) and the National Natural Science Foundation of China (No. 61375074, 61273318). The contact author is Zhifang Sui.
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Appendix
Appendix
We introduce how we implement the TEXT-E approach mentioned in Sect. 4. As [20] did, we use the most commonly used unambiguous causal verbs and connectives in Table 6 to extract causality as event associations. We did not use as and after because as is ambiguous, and after cannot guarantee that events connected by it are associated according to definition in our paper.
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Ge, T., Cui, L., Ji, H., Chang, B., Sui, Z. (2016). Discovering Concept-Level Event Associations from a Text Stream. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_34
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