Between natural language searches, and machine learning, lawyers will be able to unravel complex precedents quickly and efficiently.
Not exact matches
The work addresses several connections
between text and image in a digital world, not only
between a
search term and its results — a one way correspondence which yields an image at once to vague and too specific to be able to work reliably in the other way round — but also
between image as text (code) and the effect of
natural language on that code.
Because of the proliferation of online legal information, there is an opportunity to cut through the complexities of research by creating an intersection
between data,
natural -
language search and technology.
This means users may have to toggle
between natural language and terms and connectors
searching to make sure they are getting the best results.
How can the cost effectiveness balance be found
between Boolean
searching and sophisticated
natural language searching engines?
As these solutions have evolved over the years, changes have largely (and we are painting with a broad brush here) involved
natural language search, expansions in content, usability, connections
between content resources, and intelligent recommendations.
Find's
natural language processor facilitates
searches with typed requests like «three bedroom homes for sale in Naperville with a pool
between 600k and 800k,» or «condo in Chicago with deck.»