Grant: Engage Plus Grants Program

Social media has pervaded virtually every aspect of our lives. People use social media in many ways on a*regular basis: networking, productivity, education, navigation and everything in between. Dealing with*multilingual social media is very challenging, as we have to deal with a certain amount of languages that can*quickly become overwhelming .*UQAM's team, under the direction of Prof. Sadat Fatiha, has completed a NSERC Engage project with the*industrial partner, Advanced Symbolic Inc., entitled 'Bridging Languages in Social Networks and*Semi-Supervised Learning for a Compact Representation', which goal was pre-processing Chinese and*Japanese social media. The developed linguistic tools have shown good performances in terms of accuracy and*F-measure, outperforming other segmentation tools with up to 4.80 pts for the Japanese segmentation and up to*+2.69 for the Chinese.*The current NSERC Engage Plus project aims at pursuing the initial project, by adding and involving Korean*as a third language in ASI's Asian languages Artificial Intelligence pipeline.*In this project, our main concern is the linguistic pre-processing of South Korean short messages (140*characters limit) of Twitter that are unstructured and highly noisy. As we do not have annotated data for the*noisy set of South Korean social media data, unsupervised and transfer learning techniques will be explored*and investigated. The construction of such powerful tool and technology will help design a complete data*mining and Natural Language Processing pipeline for Asian languages at ASI, as the company has very little*expertise in solving such problems, in an automatic way.