Classifying textual data: shallow, deep and ensemble methods
This addresses text classification for customer service applications, but it is incremental as it focuses on comparative evaluation rather than introducing new methods.
This paper compared shallow, deep, and ensemble methods for text classification on a challenging real-world dataset of customer care calls from an Italian phone company, finding that deep learning outperformed classical methods and that ensemble approaches combining shallow and deep methods improved robustness and accuracy.
This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. The study is motivated by a challenging real data problem, characterized by high-dimensional and extremely sparse data, deriving from incoming calls to the customer care of an Italian phone company. We will show that deep learning outperforms many classical (shallow) strategies but the combination of shallow and deep learning methods in a unique ensemble classifier may improve the robustness and the accuracy of "single" classification methods.