Capture information from sentence flows

Grooper can read documents in paragraphs and sentences just like a human. This allows it to understand and accurately recommend correct values from the body of documents by considering the surrounding flow of language.

Examples

  • Language Element Recognition:
  • Locate all paragraphs or sentences in a document.
  • Language Element Classification:
  • Determine if a contract contains a non-solicitation clause.
  • Document Flow Detection:
  • Extract ‘Monday, May 27, 2009’ across multiple lines.
  • Context-Based Data Capture:
  • Determine if a date value is the Maturity Date or the Loan Date.
  • Powerful Language Parsing:
  • Distinguish “SW ¼ of the NW ¼” from “SW ¼ and the NW ¼”.

This technique has made it possible for us to identify all the values that make up a legal description, then break the full description into the individual tracts of land contained within the lease. There was no way we could overcome this challenge without resorting to custom development, and even then, the results were not something we could trust in a production scenario.

Paragraph Detection & Analysis

Grooper’s paragraph ranking engine assesses a document’s structure, intelligently groups words into paragraphs, then compares them against training samples to find the “best match”. Then the user is presented a recommendation list.

Lease and contract analysis is a major strength for Grooper. We have successfully built a working model that finds all of the key provisions throughout the body of the main document. Then it automatically searches for modifications to each within addendums/ exhibits and brings them in-line with the main provision. This speeds up our analysis and ensures we are correctly interpreting the data.

Spatial Analysis

Pattern-matching on its own is great for efficiently finding common values like dates, amounts, and phone numbers. But when multiple choices are found on a document, how will the system know which one is the best match?

The answer is spatial analysis. Each choice is ranked by analyzing the words and features nearby.

Spatial Analysis Pattern Matching

In the example above, we are easily able to differentiate information pertaining to the borrower vs. co-borrower through radial spatial analysis.

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