A Feasibility Experiment on the Application of Predictive Coding to Instant Messaging Corpora
This provides a practical, cost-effective method for legal document classification in instant messaging corpora, though it appears incremental in its technical approach.
The paper tackled the challenge of applying predictive coding (document classification) to informal instant messaging data by grouping messages into day chats and using feature selection with logistic regression, achieving an economically feasible solution with demonstrated cost savings on an Instant Bloomberg dataset.
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.