An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool
| Autor: | Andrea Ciapetti, Giulia Ruggiero, Luigi Lomasto, Daniele Toti, Rosario Di Florio, Giuseppe Miscione |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Basis (linear algebra) Computer science business.industry Process (engineering) Software tool Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI 02 engineering and technology Machine learning computer.software_genre Hierarchical database model 020204 information systems Text classification 0202 electrical engineering electronic engineering information engineering Classification methods Embedding Document embedding 020201 artificial intelligence & image processing Artificial intelligence business computer Neural networks Taxonomies |
| Zdroj: | Enterprise Information Systems ISBN: 9783030407827 ICEIS (Revised Selected Papers) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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