Abstract
Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining. Recently, pre-trained language models (e.g., BERT) yield state-of-the-art results and dominate in a variety of NLP tasks. However, these models are incapable of imposing external knowledge in domain-specific extraction. Considering the prior knowledge of frequent n-grams that represent cause/effect events may benefit both event and causality extraction, in this paper, we propose convolutional knowledge infusion for frequent n-grams with different windows of length within a joint extraction framework. Knowledge infusion during convolutional filter initialization does not only help the model capture both intra-event (i.e., features in an event cluster) and inter-event (i.e., associations across event clusters) features but also boost training convergence. Experimental results on the benchmark datasets show that our model significantly outperforms the strong BERT+CSNN baseline.
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Wang, Z., Wang, H., Luo, X., Gao, J. (2021). Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_28
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DOI: https://doi.org/10.1007/978-3-030-75762-5_28
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