Invited Speaker---Prof. Yi Cai


School of Software Engineering, South China University of Technology, China

Biography: Yi Cai is a professor in South China University of Technology, and he received his PhD from the Chinese University of Hong Kong in 2009. Before he joined SCUT, he was a post-doctor of City University of Hong Kong. He was a visiting scholar of Imperial College London, Tsinghua University, City University of Hong Kong, Nanyang Technological University. He published more than 80 papers (e.g., IEEE Transactions on Knowledge and Data Engineering, Neural Networks, Decision Support Systems, INFORMS Journal on Computing, Knowledge Based Systems, AAMAS, CIKM, ER, WI, WISE and ICTAI) and 2 books. He reviewed papers from conferences and journals related to information retrieval, semantic web, recommender system, data mining and database, including TKDE, TOIT, Decision Support Systems, WWWJ, KBS, JCST, CIKM and ER. He is a program committee member of conferences, including ICWL, ICWE, WAIM, ICEBE and NDBC. He is the co-chair of Social Networking and Mining Track in EIDWT-2013, DaSem 2013 and SeCop 2015.

Speech Title: Entropy-based Term Weighting Schemes and Its Applications
Abstract: Term weighting schemes have been widely used in information retrieval and text categorization models. In this talk, we first investigate into the limitations of several state-of-the-art term weighting schemes. Considering that category-specific terms are more useful to discriminate different categories, and these terms tend to have smaller entropy with respect to these categories, we then explore the relationship between a term’s discriminating power and its entropy with respect to a set of categories. To this end, we propose two entropy- based term weighting schemes which measure the discriminating power of a term based on its global distributional concentration in the categories of a corpus. To demonstrate the effectiveness of the proposed term weighting schemes, we compare them with state-of-the-art schemes in text classification and LDA.