Management Department


Date of this Version



Zaghloul and Trimi International Journal of Quality Innovation (2017) 3:3 DOI 10.1186/s40887-017-0012-y


© 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.


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.