This work describes an automatic text classification method implemented in a software tool called NETHIC, which takes advantage of the inner capabilities of highly-scalable neural networks combined with the expressiveness of hierarchical taxonomies. As such, NETHIC succeeds in bringing about a mechanism for text classification that proves to be significantly effective as well as efficient. The tool had undergone an experimentation process against both a generic and a domain-specific corpus, outputting promising results. On the basis of this experimentation, NETHIC has been now further refined and extended by adding a document embedding mechanism, which has shown improvements in terms of performance on the individual networks and on the whole hierarchical model.
|Nome||LECTURE NOTES IN BUSINESS INFORMATION PROCESSING|
|Convegno||21st International Conference on Enterprise Information Systems, ICEIS 2019|
|Periodo||3/5/19 → 5/5/19|
- Document embedding
- Machine learning
- Neural networks
- Text classification