Natural Language Processing
Capture Information from Sentence Flows
With Grooper Natural Language Processing, find paragraphs, sentences, or other language elements in your documents that convey specific meaning.
Supervised machine learning makes training easy and transparent. Powerful n-gram analysis ensures reliable interpretation – regardless of wording differences.
Grooper reads paragraphs and sentences in documents just like a human. Leverage powerful machine understanding that accurately recommends correct values from the body of documents by considering the surrounding flow of human language. What is Grooper?
Examples of Grooper Natural Language Processing NLP:
- Language Element Recognition:
- Find all paragraphs or sentences in a document.
- Language Element Classification:
- Decide if a contract has a non-solicitation clause.
- Document Flow Detection:
- Extract ‘Monday, May 27, 2009’ across multiple lines.
- Context-Based Data Capture:
- Decide 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”.
Tackle your most difficult problems in natural language processing. Power through large amounts of documents and data quickly and efficiently.
Why This NLP Method?
Working with legal documents like contracts or leases? This technique makes it possible for you to find specific provisions or legal descriptions and then break them down into the data you need. Abstract any data more quickly – find dates, individual tracts of land, legal clauses, and more.
Now you don’t need custom development or multiple data science tools. Fuel your workflows with production-quality results.
Grooper natively processes text as n-grams and via porter stemming in addition to supporting configurations that implement more complex NLP methods such as:
- Sentiment analysis
- Part-of-speech tagging
- Named entity tagging
- Feature-based tagging
The main difference between a standard NLP library (like the Stanford Library) and Grooper is the use of NLP throughout the product, not just as an add-on. NLP and other advanced ML / AI functionality is embedded throughout the solution.
Although the learning curve is high, it isn’t too much more than other products.
Grooper’s approach to image and document analysis is not comparable to any other in the space. The intelligent analysis is fantastic and reduces man hours with growing return on investment.”
– Jeff C.
Paragraph Detection & Analysis
The Grooper paragraph ranking engine looks at a document’s structure and intelligently groups words into paragraphs.
It then compares them against training samples to find the “best match,” and presents a recommendation list.
Indentions, double spacing, bullets, key phrases, line length, and many other factors must be considered to determine where each paragraph starts and stops.
Grooper provides an easy to configure console in order to tune paragraph detection settings for each project.
Use the full power of Grooper data types to collect features from within each paragraph. These can be n-grams, entries from a lexicon, or a non-value feature count grouped by data types like: address, phone number, name, etc.
The analysis spans lines of text to ensure accurate multi-word feature collection regardless of line wrap.
How Does Grooper Analyze Leases & Contracts?
Lease and contract analysis is a classic use-case for automation. Abstraction used to be a very manual and time consuming process. Data classification and extraction models find all key provisions within legal documents.
The software is trained to look for modifications within addendums / exhibits and associate them with the main provision.
This speeds up analysis and ensures you are correctly interpreting the data. Learn more about how to intelligently analyze leases and contracts below:
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.
In the example above, you will easily find the separate data that pertains to the borrower vs. co-borrower through radial spatial analysis.
In structured documents, most data is recorded in label/value pairs. This means that a value has a corresponding label somewhere on the page that tells its meaning.
And field labels are generally written above and/or to the left of the value they define.
Grooper ranks possible values by looking spatially in one or more general directions and provides a confidence level percentage.
Radial Analysis and Geotagging
Considering the nearby words, and also the direction each word is located in relation to the candidate, leads to better accuracy identifying field values from documents.
Geotagging increases this, and it allows for filtering features based on direction as a simple way to remove features not likely to define a value.