Erik Cambria
NTU Nanyang Technological University Singapore

Abstract:

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.

Erik Cambria is Founder of SenticNet, a Singapore-based company offering B2B sentiment analysis services, and Associate Professor at NTU Nanyang Technological University Singapore.

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Venue
Department of Management, Economics and Industrial Engineering
Building B26/B – Room 3.23 – third floor
Via Lambruschini 4/B, Milano