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Karin held previous posts as the Scientific Director of Health and Life Sciences at NICTA Victoria Research Laboratory, at the University of Colorado School of Medicine, and Los Alamos National Laboratory.
Bio: Karin Verspoor is a Professor in the School of Computing and Information Systems and Deputy Director of the Health and Biomedical Informatics Centre at the University of Melbourne.
Trained as a computational linguist, Karin’s research primarily focuses on extracting information from clinical texts and the biomedical literature using machine learning methods to enable biological discovery and clinical decision support.
Bio: Ryan is a lecturer (≈assistant professor) of computer science at the University of Cambridge. in Spring 2019 from the computer science department of the Johns Hopkins University, where he was affiliated with the Center for Language and Speech Processing; he was co-advised there by Jason Eisner and David Yarowsky. student at the Center for Information and Language Processing at LMU Munich supported by a Fulbright Fellowship and a DAAD Research Grant under the supervision of Hinrich Schütze.
He specializes in natural language processing, computational linguistics and machine learning, focusing on deep learning and statistical approaches to phonology, morphology, linguistic typology and low-resource languages. He has received best paper awards at ACL 2017 and EACL 2017 and two honorable mentions for best paper at EMNLP 2015 and NAACL 2016. His Ph D was supported by an NDSEG graduate fellowship, the Fredrick Jelinek Fellowship, and a Facebook Fellowship.
We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches.
We provide a comprehensive suite of experiments on over 200 distinct languages.Human-Computer Dialogue Systems: Building systems to allow spoken language interaction with computers or embodied conversational agents, with applications in areas such as keyboard-free access to information, games and entertainment, articifial companions. Bio: Daniel is a Lecturer at The University of Melbourne.Detection of Reuse and Anomaly: Investigating techniques for determining when texts or portions of texts have been reused or where portions of text do not fit with surrounding text. His main research topic is Natural Language Generation, with a focus on Machine Translation.His main research interests include computational social science, information retrieval, and data mining.He holds his Ph D from the School of Computing at Dublin City University (DCU), Ireland.He also has a set of 9 patents filed under his name.Some of his work was featured in popular press, such as CNN, BBC, Washington Post, National Geographic, and MIT Tech reviews.4 April 2019 - Walid Magdy (University of Edinburgh) - Online Users' Behaviour Understanding and Prediction with Data Science Large concern by public has emerged recently about social media data can reveal about users.In this talk, some examples are presented of how “public” social media data could be explored with data science to predict users’ behaviour and societies trends, including public interest, individual preferences, and personal information.The NLP group has close associations with the Speech and Hearing and Information Retrieval research groups which carry out research into other areas of computational processing of human language. His personal webpage can be found at https://beckdaniel.and he tweets at https://twitter.com/beck_daniel - Karin Verspoor (University of Melbourne) - Natural Language Processing (NLP) for structuring complex biomedical texts: progress and remaining challenges The NLP community has been focused on methods for identifying and extracting key concepts and relations from highly specialised and terminology-rich texts; these texts have posed a challenge to general NLP tools as well as providing an opportunity to explore the robustness of relation extraction methods to domain-specific applications.In this talk I will present our recent studies with graph kernels and neural methods for relation extraction from the biomedical literature, present empirical work on core supporting tasks such as syntactic analysis of these texts, and discuss open challenges for work in this direction and beyond.