Year: 2019
Type: article
Abstract: This paper discusses methods to improve the performance of text classification on data that is difficult to classify due to a large number of unbalanced classes with noisy examples.A variety of features are tested, in combination with three different neural-network-based methods with increasing complexity.The classifiers are applied to a songtext-artist dataset which is large, unbalanced and noisy.We come to the conclusion that substantial improvement can be obtained by removing unbalancedness and sparsity from the data.This fulfils a classification task unsatisfactorily-however, with contemporary methods, it is a practical step towards fairly satisfactory results.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Universität Stuttgart
OpenAlex ID: W3089073069