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Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126

2018-04-09 · 46 min · episode 126 · 13 entities

episode page audio

Asserted relationships

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    Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126
  • → hosted by Sam Charrington person
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    Feed author/publisher: Sam Charrington
  • → discusses Science concept
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  • → discusses Technology company
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    Feed category: Tech News
  • → hosted by TWIML company
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    Feed author/publisher: TWIML

Entities found in this episode

concepts 5

  • mentioned Science concept
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    Feed category: Science
  • mentioned Tech News concept
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    evidence rules-v4
    Feed category: Tech News
  • discusses Science concept
    0.40
    evidence rules-v4
    Feed category: Science
  • discusses News concept
    0.40
    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
    0.40
    evidence rules-v4
    Feed category: Tech News

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Technology company
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    evidence rules-v4
    Feed category: Technology
  • discusses Technology company
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    evidence rules-v4
    Feed category: Technology
  • hosted by TWIML company
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    evidence rules-v4
    Feed author/publisher: TWIML

persons 3

  • mentioned David Rosenberg person
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    Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126
  • mentioned Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.” Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, David and his team have implemented a deep learning pipeline for extracting data from the documents. In our conversation, we dig into the information extraction process, including how it was built, how they sourced their training data, why they used LaTeX as an intermediate representation and how and why they optimize on pixel-perfect accuracy. There’s a lot of interesting info in this show and I think you’re going to enjoy it. The notes for this show can be found at twimlai.com/talk/126.