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

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

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

Content Provider	ACM Digital Library
Author	Jung, YoungHoon Stratos, Karl Carloni, Luca P.
Abstract	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.
Starting Page	538
Ending Page	548
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741633
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personal search Sns Information extraction Ner Neural network Opencl
Content Type	Text
Resource Type	Article

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