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2016 Hybrid Information Extraction Systems

      • Teaching
      • 2023 Neural Networks Architecture
      • 2022 Neural Networks Architecture
      • 2021 Machine Learning over Encrypted Data
      • 2020 Feature Engineering for Data Engineering Guest Lecture
      • 2020 Feature Engineering for Spatial and Temporal Data
      • 2020 Feature Engineering and human-in-the-loop in Data Science Guest Lecture
      • 2019 Seminar The Importance of Domain Expertise in Data Science and Feature Engineering
      • 2018 NLG Traditional Approaches and Research Directions
      • 2018 Feature Engineering
      • 2017 Hybrid Information Extraction Systems
      • 2016 Hybrid Information Extraction Systems
      • 2014 Big Data Machine Learning
      • 2013 ML Workshop
      • 2011 FaMAF Intro to NLG
  1. Teaching
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  3. 2016 Hybrid Information Extraction Systems

This course was a 15h, 5 -day course at the Escuela de Ciencias Informáticas organized by the Universidad de Buenos Aires.

The material included background information about traditional IE and ML (statistical) IE, with techniques combining them using Apache UIMA. The engineering knowledge required to follow the examples was intense. While the course was well received, it is unclear it helped the audience given its complexity.

The examples came from the IE4OpenData project and the hope was to recruit more people interested in the project, which did not materialize after the fact.

The slides of the course are available on GitHub.

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