Natural Language Processing and Text Mining with Graph-Structured Representations

Bang LIU (bang3 <at> ualberta (point) ca)

University of Alberta

Le 13 février 2020 à 10 h 30 — !!! date inhabituelle !!!

Salle 3195, Pavillon André-Aisenstadt

In this talk, I will share with the audience my research experiences on a range of NLP tasks, including text matching, text mining, and text generation. I will demonstrate that the graph is a natural way to capture the connections between different text objects, such as words, entities, sentences, and documents. By combining graph-structured representations of text objects at various granularities with Graph Neural Networks (GNNs), significant benefits can be brought to various NLP tasks. Finally, I will share my experience in deploying our algorithms in industry applications, such as Tencent QQ Browser, Mobile QQ and WeChat, for hot event discovery, query and document understanding, as well as news feeds recommendation.

Biographie :

Bang Liu received his MSc and PhD degrees in Computer Engineering from the University of Alberta (Canada), and his B.E. degree in Electrical Engineering from University of Science and Technology of China. His research interests primarily lie in the areas of natural language processing (NLP), data mining, and applied machine learning. Bang has produced visible values to both academia and industry. His innovations have been deployed in real-world applications, serving over a billion daily active users. He has 15 papers published or accepted by top conferences and journals such as SIGMOD, ACL, KDD, WWW, ICDM, CIKM, TKDD, etc., as well as multiple manuscripts under submission. His research on NLP had helped his co-supervisor's research team win the Extraordinary Achievement Award for 2016-2017 CCF-Tencent Rhino Bird Open Grant.

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