SRNA: Semantics-aware Recurrent Neural Architecture for classification of document fragments

Tracking #: 1858-3071

This paper is currently under review
Authors: 
Blaz Skrlj
Jan Kralj
Nada Lavrač
Senja Pollak

Responsible editor: 
Guest Editors Semantic Deep Learning 2018

Submission type: 
Full Paper
Abstract: 
Deep neural networks are becoming ubiquitous for natural language processing tasks, such as document classification and translation. Semantic resources, such as word taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semantic data mining approach, which converts semantic information - related to a given set of documents - into a set of novel features that are used for learning. A recurrent deep neural network architecture is also proposed, enabling the system to learn in parallel from the semantic vectors and from the vectorized documents. The experiments show that the proposed approach outperforms the approach without semantic knowledge, where the main gain in accuracy is observed on the documents of reduced length. We showcase the effectiveness of the proposed approach on the topic categorization, sentiment analysis and gender profiling tasks.
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