Abstract
Supervised machine learning methods have been widely used in relation extraction to find the relation between two named entities in a sentence. However, the disadvantages of supervised machine learning methods are that constructing the training data set is costly and time-consuming, and the machine learning system is ultimately dependent on the specific domain of the training data. To overcome these disadvantages, we propose a two-step relation extraction model with distant supervision. The two-step model consists of a one-class model and a multi-class model. The one-class model selects positive sentences from input sentences and the multi-class model classifies the positive sentences into specific classes. In the experiments, the proposed model showed good F1-measures (62.9 % in the auto-labeled test data, 63.8 % in the gold-labeled test data), although it does not use any human-labeled training data.
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Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A1A4A01005074). This research was also supported by LG Electronics.
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Choi, M., Lee, Hg. & Kim, H. Relation extraction based on two-step classification with distant supervision. J Supercomput 72, 2609–2622 (2016). https://doi.org/10.1007/s11227-015-1535-4
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DOI: https://doi.org/10.1007/s11227-015-1535-4