Erik Cambria received his PhD in Computing Science and Mathematics in 2012 following the completion of an EPSRC project in collaboration with the MIT Media Lab, which was selected as impact case study by the University of Stirling for the UK Research Excellence Framework. After working at HP Labs India and Microsoft Research Asia, in 2014 he joined Nanyang Technological University as an assistant professor.
Dr Cambria is associate editor of several journals, e.g., NEUCOM, INFFUS, KBS, AIRE, IEEE CIM and IEEE Intelligent Systems, where he manages the Department of Affective Computing and Sentiment Analysis. He is founder of SenticNet, a spinoff offering B2B sentiment analysis services (http://business.sentic.net), and is recipient of many awards, e.g., AI's 10 to Watch. Dr Cambria is involved in several international conferences as PC member, e.g., AAAI, IJCAI, UAI, ACL, and EMNLP, workshop organizer, e.g., ICDM SENTIRE, and invited speaker, e.g., IEEE SSCI 2017.
Symbolic and Subsymbolic AI for Sentiment Analysis
by Assistant Professor, Nanyang Technological University
With the recent developments of deep learning, AI research has gained new vigor and prominence. However, machine learning still faces three big challenges: (1) it requires a lot of training data and is domain-dependent; (2) different types of training or parameter tweaking leads to inconsistent results; (3) the use of black-box algorithms makes the reasoning process uninterpretable. At SenticNet, we address such issues in the context of NLP via sentic computing, a multidisciplinary approach that aims to bridge the gap between statistical NLP and the many other disciplines necessary for understanding human language such as linguistics, commonsense reasoning, and affective computing. Sentic computing is both top-down and bottom-up: top-down because it leverages symbolic models such as semantic networks and conceptual dependency representations to encode meaning; bottom-up because it uses subsymbolic methods such as deep neural networks and multiple kernel learning to infer syntactic patterns from data.