Dr. Reza Haffari will visit our lab tomorrow, Friday 3rd of July. He will give a talk on Optimizing Multivariate Performance Measures for Learning Relation Extraction Models. The talk will be at 1PM at TASC1 9204 West. Here is the abstract and a short bio:
Title: Optimizing Multivariate Performance Measures for Learning Relation Extraction Models
Abstract: We describe a novel max-margin learning approach to optimize non-linear performance measures for distantly-supervised relation extraction models. Our approach can be generally used to learn latent variable models under multivariate non-linear performance measures, such as Fβ-score. Our approach interleaves Concave-Convex Procedure (CCCP) for populating latent variables with dual decomposition to factorize the original hard problem into smaller independent sub-problems. The experimental results demonstrate that our learning algorithm is more effective than the ones commonly used in the literature for distant supervision of information extraction models. On several data conditions, we show that our method outperforms the baseline and results in up to 8.5% improvement in the F1-score.
Bio: Reza Haffari is an Assistant Prof. in the Faculty of IT, Monash University. His research is in the intersection of Machine Learning and Natural Language Processing (NLP). His primary research is developing new models and learning algorithms for real-life problems, particularly those arise in NLP. This includes topics like structured prediction, domain adaptation, and semi-supervised learning for problems such as machine translation, parsing, language modelling, and information extraction.