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Open AccessArticle

Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture

by Blaž Škrlj 1,2, Jan Kralj 1, Nada Lavrač 1,3 and Senja Pollak 1,4,*
1
Jožef Stefan Institute, 1000 Ljubljana, Slovenia
2
Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
3
School of Engineering and Management, University of Nova Gorica, 5000 Nova Gorica, Slovenia
4
Usher Institute, Medical School, University of Edinburgh, Edinburgh EH16 4UX, UK
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(2), 575-589; https://doi.org/10.3390/make1020034
Received: 21 February 2019 / Revised: 28 March 2019 / Accepted: 1 April 2019 / Published: 4 April 2019
(This article belongs to the Section Learning)
Deep neural networks are becoming ubiquitous in text mining and natural language processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semantic text mining approach, which converts semantic information related to a given set of documents into a set of novel features that are used for learning. The proposed Semantics-aware Recurrent deep Neural Architecture (SRNA) enables the system to learn simultaneously from the semantic vectors and from the raw text documents. We test the effectiveness of the approach on three text classification tasks: news topic categorization, sentiment analysis and gender profiling. The experiments show that the proposed approach outperforms the approach without semantic knowledge, with highest accuracy gain (up to 10%) achieved on short document fragments. View Full-Text
Keywords: recurrent neural networks; text mining; semantic data mining; taxonomies; document classification recurrent neural networks; text mining; semantic data mining; taxonomies; document classification
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Škrlj, B.; Kralj, J.; Lavrač, N.; Pollak, S. Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture. Mach. Learn. Knowl. Extr. 2019, 1, 575-589.

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