Classifying business summaries against a hierarchical industry classification structure using supervised machine learning

Abstract

A classification system is provided for classifying text-based business summaries, referred to herein as 'summaries,' against a hierarchical industry classification structure. The classification system includes a word-based sub classifier that uses a neural network to generate a vector space for each summary in a training set, where each summary in the training set is known to correspond to a particular industry classification in the hierarchical industry classification structure. Weight values in the hidden layer of a neural network used by the word-based sub classifier are changed to improve the predictive capabilities of the neural network in the business summary classification context. Embodiments include increasing representation in the training set for underrepresented parent industry classifications and attributes of the hierarchical industry classification structure, such as distances between industry classifications and whether industry classifications are in the same subgraph. The completion of training of the word-based sub classifier is based upon whether a performance metric, such as an hFscore, satisfies one or more early stopping criteria. The classification system also includes a category-based sub classifier and a meta classifier.