Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy

Abstract: The company Ericsson is taking steps towards embracing automating techniques and applying them to their product development cycle. Ericsson wants to apply machine learning techniques to automate the evaluation of a text categorization problem of error reports, or trouble reports (TRs). An excess of 100,000 TRs are handled annually. This thesis presents two possible solutions for solving the routing problems where one technique uses traditional classifiers (Multinomial Naive Bayes and Support Vector Machines) for deciding the route through the company hierarchy where a specific TR belongs. The other solution utilizes a Convolutional Neural Network for translating the TRs into low-dimensional word vectors, or word embeddings, in order to be able to classify what group within the company should be responsible for the handling of the TR. The traditional classifiers achieve up to 83% accuracy and the Convolutional Neural Network achieve up to 71% accuracy in the task of predicting the correct class for a specific TR.