A Neural Network Component for Knowledge-Based Semantic Representations of Text

Alejandro Piad-Morffis, Rafael Muñoz, Yoan Gutiérrez, Yudivian Almeida-Cruz, Suilan Estevez-Velarde, Andrés Montoyo


Abstract
This paper presents Semantic Neural Networks (SNNs), a knowledge-aware component based on deep learning. SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures. At prediction time, SNNs provide a semantic encoding extracted from the input data, which can be exploited by other neural network components to build extended representation models that can face alternative problems. The SNN architecture is defined in terms of the concepts and relations present in a knowledge base. Based on this architecture, a training procedure is developed. Finally, an experimental setup is presented to illustrate the behaviour and performance of a SNN for a specific NLP problem, in this case, opinion mining for the classification of movie reviews.
Anthology ID:
R19-1105
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
904–911
Language:
URL:
https://aclanthology.org/R19-1105
DOI:
10.26615/978-954-452-056-4_105
Bibkey:
Cite (ACL):
Alejandro Piad-Morffis, Rafael Muñoz, Yoan Gutiérrez, Yudivian Almeida-Cruz, Suilan Estevez-Velarde, and Andrés Montoyo. 2019. A Neural Network Component for Knowledge-Based Semantic Representations of Text. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 904–911, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
A Neural Network Component for Knowledge-Based Semantic Representations of Text (Piad-Morffis et al., RANLP 2019)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/R19-1105.pdf