以色列沙蒙工程学院Marina Litvak博士和Natalia Vanetik博士应计算机学院智能科学与技术中心李蕾副教授邀请，将于2018年9月19日来BET356体育官方网站登录沙河校区作学术报告。欢迎有兴趣的师生踊跃参与。
1. 讲座题目：Introduction to Multilingual Text Analysis
主讲人：Marina Litvak
主持人：李蕾副教授
时间：2018年 9月19日（周三）下午15:00
地点：沙河校区教学楼N206教室
摘要：
Text analytics is a very wide research area. Its overarching goal is to discover and present knowledge–facts, rules, and relationships–that is otherwise hidden in textual content and unattainable by automated processing. Prior to applying analytical methods, text needs to be turned into structured data through the application of natural language processing (NLP). Then, data mining techniques, including link and association analysis, visualization, and predictive analytics, can be applied to the structured input in order to produce a requested output. In this talk, I give an overview of tasks and techniques required for typical text analysis tasks, and discuss the challenges which one needs to overcome in order to apply them to multiple languages and adapt to a multilingual domain.
主讲人介绍：
Marina Litvak has obtained a Ph.D. in Information Systems Engineering from Ben-Gurion University of the Negev. She is currently a faculty member at Department of Software Engineering of Shamoon College of Engineering in Beer Sheba, Israel. Her research interests include information retrieval, text mining, automated summarization, and social media analysis. She is a member of the Association for Computational Linguistics. Marina is a co-designer of the MUSE summarizer and the MUSEEC system, which are known summarization systems in the multilingual text summarization. She serves as a program committee member and a reviewer in the summarization and multilinguality tracks in the ACL sponsored conferences. Marina is a co-organizer of the MultiLing contest since 2011.
2. 讲座题目：Survey of text models
主讲人：Natalia Vanetik
主持人：李蕾副教授
时间：2018年 9月19日（周三）下午15:00
地点：沙河校区教学楼N206教室
摘要：
In the area of Text Analysis (TA), construction of a mathematical model that represents text bears great importance. This model, often referred to as a text representation model, determines how well TA algorithms will work. Initial "rise to fame" of what we currently know as Natural Language Processing (NLP) began with statistical models of text that gave good results for typical TA tasks such as text categorization, text clustering and text summarization, and more. In this talk, I present several text representaion models in historical order, such as bag-of-words, vector space model, n-grams, integer linear programming text representation and the polytope model. Additionally, I will show how these models can be used to formulate TA tasks as optimization problems.
主讲人介绍：
Natalia Vanetik is a currently a faculty member at Department of Software Engineering of Shamoon Academic College of Engineering in Beer Sheva, Israel. Her main field of interest is Text Analysis, focusing on multilingual Text Summarization, and unsupervised optimization methods. She is the author of multiple scientific publications in international peer-reviewed conferences and refereed journals. She has served on the program committee for several international conferences. She has obtained her PhD in Computer Science from Ben-Gurion University of the Negev in 2009 in the field of Graph Mining and Combinatorial Optimization. Since 2010, she has been involved in teaching activities at Shamoon Academic College of Engineering, more specifically she teaches multiple courses at the undergraduate degree in Software Engineering and the master’s degree in Software Engineering, with focus on courses in fields of Databases, Algorithms and Data Mining.
该讲座为前沿讲座，欢迎全校师生踊跃参加。
计算机学院
校学术委员会
2018年9月13日
