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• W1931477211 abstract "Like in other NLP tasks, it has been claimed that advances of machine learning (ML) based approaches to relation extraction (RE) are hampered by the imbalanced distribution of positive and negative instances in the annotated training data. Usually, the number of negative instances is much larger than that of the positive ones and such skewness also exists in the test data. In this paper, we aim at addressing the problem of imbalanced distribution by automatically curbing less informative negative instances. We propose some criteria for identifying such instances and incorporate them in an existing state-of-the-art RE approach. Empirical results on 5 benchmark biomedical corpora show that our proposed approach improves both recall and F1 scores. At the same time, there is a large drop in the number of negative instances and in execution runtime as well." @default.
• W1931477211 created "2016-06-24" @default.
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• W1931477211 date "2012-12-01" @default.
• W1931477211 modified "2023-09-24" @default.
• W1931477211 title "Impact of Less Skewed Distributions on Efficiency and Effectiveness of Biomedical Relation Extraction" @default.
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