Lesson plan

Table extraction

Table extraction is the process of recognizing and separating a table from a large document, possibly also recognizing individual rows, columns or elements. It may be regarded as a special form of information extraction. Table extractions from webpages can take advantage of the special HTML elements that exist for tables, e.g., the "table" tag, and programming libraries may implement table extraction from webpages. The Python pandas software library can extract tables from HTML webpages via its read_html() function. More challenging is table extraction from PDFs or scanned images, where there usually is no table-specific machine readable markup. Systems that extract data from tables in scientific PDFs have been described. Wikipedia presents some of its information in tables, and, e.g., 3.5 million tables can be extracted from the English Wikipedia. Some of the tables have a specific format, e.g., the so-called infoboxes. Large-scale table extraction of Wikipedia infoboxes forms one of the sources for DBpedia. Commercial web services for table extraction exist, e.g., Amazon Textract, Google's Document AI, IBM Watson Discovery, and Microsoft Form Recognizer. Open source tools also exist, e.g., PDFFigures 2.0 that has been used in Semantic Scholar.…

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Article summary

Table extraction is the process of recognizing and separating a table from a large document, possibly also recognizing individual rows, columns or elements. It may be regarded as a special form of information extraction. Table extractions from webpages can take advantage of the special HTML elements that exist for tables, e.g., the "table" tag, and programming libraries may implement table extraction from webpages. The Python pandas software library can extract tables from HTML webpages via its read_html() function. More challenging is table extraction from PDFs or scanned images, where there usually is no table-specific machine readable markup. Systems that extract data from tables in scientific PDFs have been described. Wikipedia presents some of its information in tables, and, e.g., 3.5 million tables can be extracted from the English Wikipedia. Some of the tables have a specific format, e.g., the so-called infoboxes. Large-scale table extraction of Wikipedia infoboxes forms one of the sources for DBpedia. Commercial web services for table extraction exist, e.g., Amazon Textract, Google's Document AI, IBM Watson Discovery, and Microsoft Form Recognizer. Open source tools also exist, e.g., PDFFigures 2.0 that has been used in Semantic Scholar.…

Key claims

  1. publication date: 2017 — source: Wikipedia · confidence 0.95

Discussion questions

  1. Summarise the article in your own words. Which single claim would you verify first, and why?
  2. The record states that the publication date is "2017". What evidence would count against this claim?
  3. Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
  4. If two sources disagreed about one of these facts, what would you need in order to trust one over the other?

Bibliography

  1. Wikipedia https://en.wikipedia.org/w/index.php?oldid=1372139276

Key claims and bibliography are generated from the same tracked claims and sources as the article page.