Classifying Spend Descriptions with Off-the-Shelf Learning Components

Analyzing spend transactions is essential to organizations for understanding their global procurement. Central to this analysis is the automated classification of these transactions to hierarchical commodity coding systems. Spend classification is challenging due not only to the complexities of the commodity coding systems but also because of the sparseness and quality of each individual transaction text description and the volume of such transactions in an organization. In this paper, we demonstrate the application of off-the-shelf machine learning tools to address the challenges in spend classification. We have built a system using off-the-shelf SVM, logistic regression, and language processing toolkits and describe the effectiveness of these different learning techniques for spend classification.