Definition extraction: Improving Balanced Random Forests

Abstract

The article discusses methods of improving the ways of applying balanced random forests (BRFs), a machine learning classification algorithm, used to extract definitions from written texts. These methods include different approaches to selecting attributes, optimising the classifier prediction threshold for the task of definition extraction and initial filtering by a very simple grammar.