Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

Deep learning has unlocked new paths towards the emulation of the peculiarly-human capability of learning from examples. While this kind of bottom-up learning works well for tasks such as image classification or object detection, it is not as effective when it comes to natural language processing. Communication is much more than learning a sequence of letters and words: it requires a basic understanding of the world and social norms, cultural awareness, commonsense knowledge, etc.; all things that we mostly learn in a top-down manner. In this project we will integrate top-down and bottom-up learning via an ensemble of symbolic and subsymbolic AI tools, which we will apply to the interesting problem of polarity detection from text. In particular, we will integrate logical reasoning within deep learning architectures to build a commonsense knowledge base for sentiment analysis.

Faculty Supervisor:

Robert Mercer

Student:

Partner:

Nanyang Technological University

Discipline:

Computer science

Sector:

Education

University:

The University of Western Ontario

Program: