Extending Sparse Classification Knowledge via NLP Analysis of Classification Descriptions

摘要

Supervised machine learning algorithms, particularly those operating on free text, depend upon the quality of their training datasets to correctly classify unlabeled text instances. In many cases where the classification task is nontrivial, it is difficult to obtain a large enough set of training data to achieve good classification accuracy. In this work we examine one such case in the context of a system designed to ground free text to an organizational hierarchy which is ontologically modeled. We explore the impact of utilizing information garnered from a highly customized Natural Language Processing (NLP) analysis of this ontology to augment a very sparse initial training dataset and compare this to a more labor intensive extraction of a small set of key words and phrases associated with each concept. We demonstrate an approach with significant improvement in classifier performance for concepts having little or no initial training data coverage.