Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK
Roxana Danger

Roxana Danger
Data scientist, reed.co.uk

Website

In her research career, Roxana Danger has often pursued and achieved the dual goal of improving the performance of information extraction systems while proposing and validating novel mechanisms for storing and analyzing the extracted data in semantic knowledge databases. Roxana is currently working as a data scientist at ReedOnline LTD, designing and applying machine learning and NLP techniques for providing data-driven insights to the company. She was previous enrolled as a research associate at the Computing Department of Imperial College London, where she designed and implemented a provenance platform and data mining tools for diagnosis decision support in health care systems, as part of EU-FP7 project TRANSFoRm, and at the Department of Computer Systems and Computation at Universidad Polit├ęcnica de Valencia, Spain, where she worked on the development of an information extraction system for protein-protein interactions. Roxana holds a PhD from University Jaume I, Castellon, Spain, where her project aimed at extracting and analyzing semantic data from archaeology site excavation reports, and undergraduate and master’s degrees in computer science from Universidad de Oriente, Santiago de Cuba.

Sessions

16:00–16:30 Wednesday, 1/06/2016
Hardcore data science
Location: Capital Suite 4 Level: Intermediate
Tags: text
Roxana Danger (reed.co.uk)
Average rating: ***..
(3.60, 5 ratings)
One of the main challenges organizations face is the semantic categorization of textual data. Roxana Danger offers an overview of ROOT, the reed online occupational taxonomy, which was constructed to improve the quality of services at reed.co.uk, and discusses this semisupervised methodology for generating (and maintaining) taxonomies from large collections of textual data. Read more.