Location: LG01, Henley Business School, Whiteknights
Presenter: Prof. Yulan He, University of Warwick, UK
Prof. He will be presenting a series of recent research work on using neural topic models for text analytics. This includes a topic-dependent attention model for sentiment classification and the extraction of coherent aspect-sentiment clusters despite using no aspect-level annotations for training; a reinforcement learning strategy of incorporating topic coherence measures as reward signals to guide the learning of a neural topic model, which is able to automatically separating background words dynamically from topic words; an unsupervised framework for jointly modelling topic content and discourse behaviours in microblog conversations and the extension of this for tracking the dynamics of topic/discourse factors for the prediction of outcome in argumentation process.
Yulan He is a Professor of Computer Science in the University of Warwick, UK. She obtained her PhD degree in spoken language understanding from the University of Cambridge. Yulan is experienced in statistical modelling and text mining, particularly the integration of machine learning and natural language processing for text analytics. She has published over 160 papers on topics including sentiment analysis, information extraction, clinical text mining, recommended systems, learning analytics and spoken dialogue systems. She has served as a (Senior) Area Chair in Sentiment Analysis in top natural language processing conferences including ACL, EMNLP and NAACL and is a Program Co-Chair of EMNLP 2020.