Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-31561-9_2
Title: Sentic neural networks: A novel cognitive model for affective common sense reasoning
Authors: Mazzocco, T.
Cambria, E. 
Hussain, A.
Wang, Q.-F.
Keywords: AI
Cognitive Modeling
Neural Networks
NLP
Sentic Computing
Issue Date: 2012
Source: Mazzocco, T.,Cambria, E.,Hussain, A.,Wang, Q.-F. (2012). Sentic neural networks: A novel cognitive model for affective common sense reasoning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7366 LNAI : 12-21. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-31561-9_2
Abstract: In human cognition, the capacity to reason and make decisions is strictly dependent on our common sense knowledge about the world and our inner emotional states: we call this ability affective common sense reasoning. In previous works, graph mining and multi-dimensionality reduction techniques have been employed in attempt to emulate such a process and, hence, to semantically and affectively analyze natural language text. In this work, we exploit a novel cognitive model based on the combined use of principal component analysis and artificial neural networks to perform reasoning on a knowledge base obtained by merging a graph representation of common sense with a linguistic resource for the lexical representation of affect. Results show a noticeable improvement in emotion recognition from natural language text and pave the way for more bio-inspired approaches to the emulation of affective common sense reasoning. © 2012 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/116780
ISBN: 9783642315602
ISSN: 03029743
DOI: 10.1007/978-3-642-31561-9_2
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