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- W2783238836 abstract "Predictive coding, the term used in the legal industry for document classification using machine learning, presents additional challenges when the dataset comprises instant messages, due to their informal nature and smaller sizes. In this paper, we exploit a data management workflow to group messages into day chats, followed by feature selection and a logistic regression classifier to provide an economically feasible predictive coding solution. We also improve the solution's baseline model performance by dimensionality reduction, with focus on quantitative features. We test our methodology on an Instant Bloomberg dataset, rich in quantitative information. In parallel, we provide an example of the cost savings of our approach." @default.
- W2783238836 created "2018-01-26" @default.
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- W2783238836 date "2017-12-01" @default.
- W2783238836 modified "2023-09-25" @default.
- W2783238836 title "A feasibility experiment on the application of predictive coding to instant messaging corpora" @default.
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- W2783238836 doi "https://doi.org/10.1109/bigdata.2017.8258386" @default.
- W2783238836 hasPublicationYear "2017" @default.
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