Machine Learning Times
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1 year ago
Getting Tabular Data from Unstructured Text with GPT-3: An Ongoing Experiment

Originally published by Roberto Rocha.

One of the most exciting applications of AI in journalism is the creation of structured data from unstructured text.

Government reports, legal documents, emails, memos… these are rich with content like names, organizations, dates, and prices. But to get them into a format that can be analyzed and counted, like a spreadsheet, usually involves days or weeks of tedious manual data entry.

Large language models like GPT-3 from OpenAI have the potential to greatly speed up this awful slog. Because these models have such a deep grasp of language (GPT-3 was trained on basically the entire internet — at least all of English Wikipedia), they can understand commands and pick out the right elements from text.

The challenge

The Canadian federal lobbyist registry has a lot of information about who is lobbying government officials, and on whose behalf. One of the most important elements of the registry is the past public offices data: lobbyists who previously worked for the government.

The data is pretty structured, showing the offices held and the time period. Here’s an example for a lobbyist working for TikTok.

To continue reading this article, click here.

4 thoughts on “Getting Tabular Data from Unstructured Text with GPT-3: An Ongoing Experiment

  1. Content such as names, organizations, dates, and prices abound in government reports, legal papers, emails, driving directions and memoranda. However, it sometimes takes days or weeks of painstaking manual data input to convert them into a format that can be evaluated and tallied, such as a spreadsheet.


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