SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis

Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, Kenneth Kwok


Abstract
In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society. While often better than its human counterparts in classification and pattern recognition tasks, however, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. In this context, the key limitations of current AI models are: dependency, reproducibility, trustworthiness, interpretability, and explainability. In this work, we propose a commonsense-based neurosymbolic framework that aims to overcome these issues in the context of sentiment analysis. In particular, we employ unsupervised and reproducible subsymbolic techniques such as auto-regressive language models and kernel methods to build trustworthy symbolic representations that convert natural language to a sort of protolanguage and, hence, extract polarity from text in a completely interpretable and explainable manner.
Anthology ID:
2022.lrec-1.408
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3829–3839
Language:
URL:
https://aclanthology.org/2022.lrec-1.408
DOI:
Bibkey:
Cite (ACL):
Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, and Kenneth Kwok. 2022. SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3829–3839, Marseille, France. European Language Resources Association.
Cite (Informal):
SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis (Cambria et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.408.pdf
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