【12.30腾讯会议】Semantic-driven Multi-label Text Representation Learning

报告题目: Semantic-driven Multi-label Text Representation Learning

报告学长: Liping Jing(景丽萍) 


报告地点:腾讯会议 ID: 381 6014 5369

摘要:Multi-label data, esp. multi-label textual data is ubiquitous in We-Media era. Comparing with the traditional single-label text, multi-label data contains complex syntactic and semantic structure. It is important and challenging to extract the discriminative information and mine the correlation among labels for improving the label prediction performance. In this talk, some recent work about multi-label text representation learning, label structure representation learning will be given.

报告人简介:Liping Jing is a Professor in Dept. of Computer Science, Beijing Jiaotong University. She earned her Ph.D degree in Applied Math. From the University of HongKong in July 2007. Prof.

Jing’s research mainly focuses on machine learning and its application in Artificial Intelligence. She has published over 100 research papers in referred international journals including IEEE TKDE, TIP, ACM TOIS, IJCV, etc. and conference proceedings including WWW, AAAI, IJCAI, CVPR, ACM MM, etc.