報告題目：Multi-Resolution Models for Learning Multilevel Abstract Representations of Text
報告時間：2019年6月15日 上午9：00
報告地點：計算機A521
報告人：Professor Xiaowei Xu
報告人簡介：
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.
報告内容簡介：
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.
主辦單位：
伟德国际BETVlCTOR
伟德国际BETVlCTOR軟件學院
伟德国际BETVlCTOR計算機科學技術研究所
符号計算與知識工程教育部重點實驗室
伟德国际BETVlCTOR國家級計算機實驗教學示範中心
