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.

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.

Abstract: A company size estimation (CSE) system predicts employee number ranges for companies based on information available in open government and website sources. The CSE system breaks down the problem into two consecutive machine learning tasks. A first operation identifies large companies and a second operation identifies employee number ranges for small and medium-sized companies. Both operations take advantage of a rich set of firmographic attributes collected for companies, such as industry codes, office locations, corporate website text, website traffic, social media presence, and discoverability with respect to various data sources.