Abstract
In the current entity relation extraction technology, more and more researchers focus on semi-supervised Bootstrapping method, because it does not require a large number of artificial tagging corpus, needs only a small amount of seed set, by self-iterative extended to obtain large-scale knowledge base. However, after a large number of iterations, there will be “semantic drift,” that is, the accuracy will reduce due to the accumulation of errors. In order to improve the accuracy of the relation instance the quality of the pattern, it is necessary to evaluate the reliability of instances and patterns. This paper uses large-scale news headline sentences set in the search engine, evaluates the reliability of instances by co-occurrence relation between description words and sentences set, then evaluates the reliability of patterns by the number of positive and negative instances in patterns historical matching record, and selects new patterns to extend and optimize. The experimental results show that the reliability evaluation of instances and patterns used in the iteration effectively improves the accuracy of relation extraction and improves the quality of the extracted pattern.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61471232).
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Qin, Z., Ye, F. (2019). Research on Reliability of Instance and Pattern in Semi-supervised Entity Relation Extraction. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_44
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DOI: https://doi.org/10.1007/978-981-10-8944-2_44
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