Sentic neural networks: A novel cognitive model for affective common sense reasoning

Citation

Mazzocco T, Cambria E, Hussain A & Wang Q (2012) Sentic neural networks: A novel cognitive model for affective common sense reasoning. In: Zhang H, Hussain A, Liu D & Wang Z (eds.) Advances in Brain Inspired Cognitive Systems: 5th International Conference, BICS 2012, Shenyang, China, July 11-14, 2012. Proceedings. Lecture Notes in Computer Science, 7366. Berlin Heidelberg: Springer, pp. 12-21. http://link.springer.com/chapter/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.

Keywords
AI; NLP; Neural Networks; Cognitive Modeling; Sentic Computing