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- W4313175513 abstract "Entity resolution (ER) is the task of identifying data instances referring to the same real-word entity. In this work, we study two major works that propose a deep-learning based solution for ER, namely DeepER and DeepMatcher. We re-evaluate their published results and benchmark it with the re-evaluated results of Magellan, an end-to-end entity resolution framework that uses classical machine learning. We then try to answer some questions mentioned in the sections below with the help of these results. We also report several interesting observations noted while reproducing these works. We highlight important but missing knowledge pertaining to generation of training and test sets. This information heavily influences the quality of evaluation results and is necessary to produce accurate authentic results. We conclude by presenting a study on ease of setting-up and rebuilding these solutions as an end-user." @default.
- W4313175513 created "2023-01-06" @default.
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- W4313175513 date "2022-10-07" @default.
- W4313175513 modified "2023-10-01" @default.
- W4313175513 title "Assessing Entity Resolution techniques based on deep learning" @default.
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- W4313175513 doi "https://doi.org/10.1109/gcat55367.2022.9971860" @default.
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