K
Kenneth Kwok
Researcher at Agency for Science, Technology and Research
Publications - 32
Citations - 1575
Kenneth Kwok is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Commonsense knowledge & Sentiment analysis. The author has an hindex of 18, co-authored 32 publications receiving 1148 citations. Previous affiliations of Kenneth Kwok include Institute of High Performance Computing Singapore & National University of Singapore.
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Proceedings Article
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings
TL;DR: This work couple sub-symbolic and symbolic AI to automatically discover conceptual primitives from text and link them to commonsense concepts and named entities in a new three-level knowledge representation for sentiment analysis.
Proceedings ArticleDOI
SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis
TL;DR: This work integrates logical reasoning within deep learning architectures to build a new version of SenticNet, a commonsense knowledge base for sentiment analysis, and applies it to the interesting problem of polarity detection from text.
Journal ArticleDOI
Functional cortical connectivity analysis of mental fatigue unmasks hemispheric asymmetry and changes in small-world networks
TL;DR: The results indicate that functional network organization can change over relatively short time scales with mental fatigue, and that decreased connectivity has a meaningful relationship with individual difference in behavior and performance.
Proceedings ArticleDOI
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
TL;DR: Two variants of DATNet are investigated to explore effective feature fusion between high and low resource, and a novel Generalized Resource-Adversarial Discriminator (GRAD) is proposed to address the noisy and imbalanced training data.
Proceedings ArticleDOI
A graph-based approach to commonsense concept extraction and semantic similarity detection
TL;DR: This work proposes an approach for effective multi-word commonsense expression extraction from unrestricted English text, in addition to a semantic similarity detection technique allowing additional matches to be found for specific concepts not already present in knowledge bases.