An Automatic Text Classification Method Based on Hierarchical Taxonomies, Neural Networks and Document Embedding: The NETHIC Tool
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Abstract (arXiv)
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.
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References · 8
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- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
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- 2014Distributed Representations of Sentences and Documentscited by 678
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- 2014A Convolutional Neural Network for Modelling Sentencescited by 163
- 2015A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classificationcited by 92
- 2016MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classificationcited by 4
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