15480

LN-Annote: An Alternative Approach to Information Extraction from Emails using Locally-Customized Named-Entity Recognition

YoungHoon Jung, Karl Stratos, Luca P. Carloni
Columbia University
International World Wide Web Conference (IW3C2), 2015

@inproceedings{jung2015ln,

   title={LN-Annote: An Alternative Approach to Information Extraction from Emails using Locally-Customized Named-Entity Recognition},

   author={Jung, YoungHoon and Stratos, Karl and Carloni, Luca P},

   booktitle={Proceedings of the 24th International Conference on World Wide Web},

   pages={538–548},

   year={2015},

   organization={International World Wide Web Conferences Steering Committee}

}

Download Download (PDF)   View View   Source Source   

443

views

Personal mobile devices offer a growing variety of personalized services that enrich considerably the user experience. This is made possible by increased access to personal information, which to a large extent is extracted from user email messages and archives. There are, however, two main issues. First, currently these services can be offered only by large web-service companies that can also deploy email services. Second, keeping a large amount of structured personal information on the cloud raises privacy concerns. To address these problems, we propose LN-Annote, a new method to extract personal information from the email that is locally available on mobile devices (without remote access to the cloud). LN-Annote enables third-party service providers to build a question-answering system on top of the local personal information without having to own the user data. In addition, LN-Annote mitigates the privacy concerns by keeping the structured personal information directly on the personal device. Our method is based on a named-entity recognizer trained in two separate steps: first using a common dataset on the cloud and then using a personal dataset in the mobile device at hand. Our contributions include also the optimization of the implementation of LN-Annote: in particular, we implemented an OpenCL version of the custom-training algorithm to leverage the Graphic Processing Unit (GPU) available on the mobile device. We present an extensive set of experiment results: beside proving the feasibility of our approach, they demonstrate its efficiency in terms of the named-entity extraction performance as well as the execution speed and the energy consumption spent in mobile devices.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: