报告题目：Multi-Resolution Models for Learning Multilevel Abstract Representations of Text
报告时间：2019年6月15日 上午9：00
报告地点：计算机A521
报告人：Professor Xiaowei Xu
报告人简介：
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.
报告内容简介：
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.
主办单位：
304永利集团官网入口
304永利集团官网入口软件学院
304永利集团官网入口计算机科学技术研究所
符号计算与知识工程教育部重点实验室
304永利集团官网入口国家级计算机实验教学示范中心
