Dr. Dmitry Ustalov
Graph-based representations are proven to be an effective approach for a variety of Natural Language Processing (NLP) tasks. Graph clustering makes it possible to extract useful knowledge by exploiting the implicit structure of the data. In this tutorial, we will present several efficient graph clustering algorithms, show their strengths and weaknesses as well as their implementations and applications. Then, the evaluation methodology in unsupervised NLP tasks will be discussed. Biography
Dmitry Ustalov is a post-doctoral research fellow
at the University of Mannheim, Germany. His research is focused on Computational Lexical Semantics and Crowdsourcing. In 2018 he defended his Kandidat Nauk (PhD) thesis which he worked on at the Krasovskii Institute of Mathematics and Mechanics, Russia. Dmitry's research is published in the premier international scientific conferences, such as ACL, EACL, and EMNLP. He serves as a reviewer for ACL, EMNLP, *SEM, EKAW, TextGraphs, and other high-level events.
In 2012 Dmitry founded NLPub
, the leading Russian wiki on Computational Linguistics. Since 2014 he has been co-organizing the workshop on Russian Semantic Evaluation
(RUSSE). Also, Dmitry teaches Text Analytics and Web Mining classes to master students.