Abstract
Open information extraction (OIE) aims to extract structured information from text. Specifically, it extracts arguments and their logical relationships from a sentence. However, as a syntactic feature commonly used in OIE, the tree structure of dependency parsing (DP) is often overlooked. In this paper, we propose a dependency parsing representation learning model for OIE. This model can fully represent the tree structure and edge information of DP. Then we fuse the learned DP embedding in a Graph Convolutional Network-based OIE model. The experiments show that the proposed OIE model outperforms four baselines on three open-sourced data sets.
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Zekun, L., Nianwen, N., Chengcheng, P., Bin, W. (2021). Dependency Parsing Representation Learning for Open Information Extraction. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_35
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DOI: https://doi.org/10.1007/978-3-030-82136-4_35
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