Abstract
Bootstrapping has been used as a very efficient method to extract a group of items similar to a given set of seeds. However, the bootstrapping method intrinsically has several parameters whose optimal values differ from task to task, and from target to target. In this paper, first, we will demonstrate that this is really the case and serious problem. Then, we propose self-adjusting bootstrapping, where the original seed is segmented into the real seed and validation data. We initially bootstrap starting with the real seed, trying alternative parameter settings, and use the validation data to identify the optimal settings. This is done repeatedly with alternative segmentations in typical cross-validation fashion. Then the final bootstrapping is performed using the best parameter setting and the entire original seed set in order to create the final output. We conducted experiments to collect sets of company names in different categories. Self-adjusting bootstrapping substantially outperformed a baseline using a uniform parameter setting.
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Fujiwara, S., Sekine, S. (2011). Self-adjusting Bootstrapping. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19437-5_15
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DOI: https://doi.org/10.1007/978-3-642-19437-5_15
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