报告题目：Multi-Resolution Models for Learning Multilevel Abstract Representations of Text
报告人：Professor Xiaowei Xu
Xiaowei Xu, a professor of Information Science at the University of Arkansas, Little Rock (UALR), received his Ph.D. degree in Computer Science at the University of Munich in 1998. Before his appointment in UALR, he was a senior research scientist in Siemens, Munich, Germany. His research spans data mining, machine learning, bioinformatics, database management systems and high-performance computing. Dr. Xu is a recipient of 2014 ACM SIGKDD Test of Time award for his contribution to the density-based clustering algorithm DBSCAN.
Complex semantic meaning in natural language is hard to be mined using computational approach. Deep language models learning a hierarchical representation proved to be a powerful tool for natural language processing, text mining and information retrieval. This course will cover the models for word embedding and learning representations of text for information retrieval and text mining. The topic includes an introduction of language models for word embedding. It is followed by a presentation of recent multi-resolution models that represent documents at multiple resolutions in term of abstract levels. More specifically, we first form a mixture of weighted representations across the whole hierarchy of a given word embedding model, so that all resolutions of the hierarchical representation are preserved for the downstream model. In addition, we combine all mixture representations from various models as an ensemble representation. Finally, the application for information retrieval and other text mining tasks is presented in the course.