Abstract
A classifier-based pattern selection approach for relation instance extraction is proposed in this paper. The classifier-based pattern selection approach proposes to employ a binary classifier that filters patterns that extracts incorrect entities for a given relation, from pattern set obtained using global estimates such as high frequency. The proposed approach is evaluated using two large independent datasets. The results presented in this paper shows that the classifier-based approach provides a significant improvement in the task of relation extraction against standard methods of relation extraction, employing pattern sets based on high frequency. The higher performance is achieved through filtering out patterns that extract incorrect entities, which in turn improves the precision of applied patterns, resulting in significant improvement in the task of relation extraction.
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Mandya, A., Bollegala, D., Coenen, F., Atkinson, K. (2018). Classifier-Based Pattern Selection Approach for Relation Instance Extraction. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_33
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