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Developing an innovative entity extraction method for unstructured data

Author

Listed:
  • Waleed Zaghloul

    (Valera Intelligent Systems)

  • Silvana Trimi

    (University of Nebraska)

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.

Suggested Citation

  • Waleed Zaghloul & Silvana Trimi, 2017. "Developing an innovative entity extraction method for unstructured data," International Journal of Quality Innovation, Springer, vol. 3(1), pages 1-10, December.
    • Handle: RePEc:spr:ijoqin:v:3:y:2017:i:1:d:10.1186_s40887-017-0012-y
    DOI: 10.1186/s40887-017-0012-y
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    References listed on IDEAS

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    1. Sang M. Lee & David L. Olson & Silvana Trimi, 2010. "Strategic innovation in the convergence era," International Journal of Management and Enterprise Development, Inderscience Enterprises Ltd, vol. 9(1), pages 1-12.
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