A Neural Network Component for Knowledge-Based Semantic Representations of Text
| Autor: | Yudivián Almeida-Cruz, Rafael Muñoz, Alejandro Piad-Morffis, Suilan Estevez-Velarde, Andrés Montoyo, Yoan Gutiérrez |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning Sentiment analysis 020206 networking & telecommunications 010103 numerical & computational mathematics 02 engineering and technology ENCODE computer.software_genre 01 natural sciences Knowledge base Component (UML) 0202 electrical engineering electronic engineering information engineering Semantic memory Artificial intelligence 0101 mathematics business Representation (mathematics) computer Natural language processing |
| Zdroj: | RANLP Scopus-Elsevier |
| DOI: | 10.26615/978-954-452-056-4_105 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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