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Talks & Seminars
Title: Natural Language Processing in Elsevier Publishing
Dr. Ron Daniel, Elsevier Labs
Date & Time: April 14, 2015 11:30
Venue: Conference Room KReSIT Building CSE Dept
Abstract:
This talk will give an overview of publishing industry's need for NLP technologies. The speaker will present Elsevier's Content Strategy and how NLP capabilities will serve as the core mechanism for their products of the future and will also discuss how high performance mechanisms such as databricks and Spark will serve as the core for our future NLP applications. We will end with an open discussion.
Speaker Profile:
Dr. Ron Daniel is the Director of Elsevier Labs, an R&D group which concentrates on smart content and on the future of scholarly communications. Educated as an electrical engineer, Ron has done extensive work on metadata standards such as the Dublin Core, RDF, and PRISM. Before joining Elsevier five years ago, he worked at a startup that was acquired for its automatic classification technology, and consulted on taxonomy and information management issues for nine years. Ron received his Ph.D. in Electrical Engineering from Oklahoma State University, and was a postdoctoral researcher at Cambridge University and at Los Alamos National Laboratory. Ron is bemused by the way technology reincarnates itself, specifically in the way that parallel implementations of neural networks for machine vision are currently in vogue, just as they were more than 20 years ago when he was working on them in grad school. Dr. Michelle Gregory received her PhD in Computational Linguistics in 2001 from the University of Of Colorado at Boulder. Her career has been focused on basic and applied research in the general area of computational linguistics with an active research profile in programs that employ probabilistic models of linguistic cues to understand and describe language use in a variety of contexts. From 2005-2010, Michelle worked as a Senior Research Scientist for Pacific Northwest National Lab where she used her expertise in natural language processing to understand and validate social data sources, sentiment and opinion analysis, machine learning, pattern recognition, and high performance computing. Her research was applied to multiple end-user domains, including cyber security, insider threat, disaster response, and others. Michelle currently is the V.P. Of Content and Innovation at Elsevier where she stewards Elsevier's Smart Content and enrichment capabilities to support the scientific and medical health professional communities. Dr. Johannes Leveling joined Elsevier as a natural language processing expert in the core capabilities group in Amsterdam last year. He obtained his PhD from the University of Hagen in Germany in 2006 (topic: natural language interfaces to bibliographic databases). He worked as a postdoctoral researcher in Hagen on question answering and geographic information retrieval using a semantic network representation before he joined the Centre for Next Generation Localisation (CNGL) in Dublin at Dublin City University in 2008. His research in CNGL focused on personalised information retrieval and adaptation of search. As a research fellow at DCU, he researched finding near-duplicate content in large document collections, identifying sensitive information in corporate documents, detecting cyberbullying in social media, and creating linguistic fingerprints to predict stock price changes.
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