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eMailMe: A Method to Build Datasets of Corporate Emails in Portuguese

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  • Akira A. de Moura Galvão Uematsu

    (Engenharia de Computação e Sistemas Digitais, Escola Politécnica-Universidade de São Paulo, Av. Prof. Luciano Gualberto, São Paulo 05508-010, Brazil)

  • Anarosa A. F. Brandão

    (Engenharia de Computação e Sistemas Digitais, Escola Politécnica-Universidade de São Paulo, Av. Prof. Luciano Gualberto, São Paulo 05508-010, Brazil)

Abstract

One of the areas in which knowledge management has application is in companies that are concerned with maintaining and disseminating their practices among their members. However, studies involving these two domains may end up suffering from the issue of data confidentiality. Furthermore, it is difficult to find data regarding organizations processes and associated knowledge. Therefore, this paper presents a method to support the generation of a labeled dataset composed of texts that simulate corporate emails containing sensitive information regarding disclosure, written in Portuguese. The method begins with the definition of the dataset’s size and content distribution; the structure of its emails’ texts; and the guidelines for specialists to build the emails’ texts. It aims to create datasets that can be used in the validation of a tacit knowledge extraction process considering the 5W1H approach for the resulting base. The method was applied to create a dataset with content related to several domains, such as Federal Court and Registry Office and Marketing, giving it diversity and realism, while simulating real-world situations in the specialists’ professional life. The dataset generated is available in an open-access repository so that it can be downloaded and, eventually, expanded.

Suggested Citation

  • Akira A. de Moura Galvão Uematsu & Anarosa A. F. Brandão, 2023. "eMailMe: A Method to Build Datasets of Corporate Emails in Portuguese," Data, MDPI, vol. 8(8), pages 1-12, July.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:8:p:127-:d:1207007
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    References listed on IDEAS

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    1. Emil Hristov & Dessislava Petrova-Antonova & Aleksandar Petrov & Milena Borukova & Evgeny Shirinyan, 2023. "Remote Sensing Data Preparation for Recognition and Classification of Building Roofs," Data, MDPI, vol. 8(5), pages 1-19, April.
    2. Talal Alshammari & Nasser Alshammari & Mohamed Sedky & Chris Howard, 2018. "SIMADL: Simulated Activities of Daily Living Dataset," Data, MDPI, vol. 3(2), pages 1-13, April.
    3. Gonçalo Carnaz & Mário Antunes & Vitor Beires Nogueira, 2021. "An Annotated Corpus of Crime-Related Portuguese Documents for NLP and Machine Learning Processing," Data, MDPI, vol. 6(7), pages 1-11, June.
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