Department of Management
Date of this Version
2017
Citation
Zaghloul and Trimi International Journal of Quality Innovation (2017) 3:3 DOI 10.1186/s40887-017-0012-y
Abstract
The main goal of this study is to build high-precision extractors for entities such as Person and Organization as a good initial seed that can be used for training and learning in machine-learning systems, for the same categories, other categories, and across domains, languages, and applications. The improvement of entities extraction precision also increases the relationships extraction precision, which is particularly important in certain domains (such as intelligence systems, social networking, genetic studies, healthcare, etc.). These increases in precision improve the end users’ experience quality in using the extraction system because it lowers the time that users spend for training the system and correcting outputs, focusing more on analyzing the information extracted to make better data-driven decisions.
Included in
Business Administration, Management, and Operations Commons, Management Sciences and Quantitative Methods Commons, Strategic Management Policy Commons
Comments
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License. Used by permission.