Everyday Language Analysis Gets Embedded
Mind your language, the system is analyzing it
25 September 2001
Everyday language analysis is different, and more complex, than the analysis of well-written and relatively structured documents.
This week Banter, a provider of intelligent everyday language analysis technologies, is releasing its Relationship Modeling Engine (RME) 5.0, designed to be easily embedded in any software application that handles unstructured information and free text, reducing engineering costs and time to market, while providing a full suite of language analysis capabilities.
RME 5.0 offers software manufacturers and integrators an expanded range of supported languages and a more refined level of accuracy through significantly greater analytical capability and tools.
RME 5.0 is distinctively positioned to analyze and understand the content of documents and messages written in everyday language, including the naturally occurring variations in expressions, spelling and grammar typically present in such text.
Typical uses for RME range from e-mail analysis, chat support, online self-service and natural language information retrieval, to document classification and tagging.
Features include Natural Language Processing (NLP) of imperfect content; analytical and statistical semantic modeling and content classification; and a unique feedback mechanism that enables real-time learning, self-maintenance and rapid, automatic adaptation to constantly changing business environments.
"RME 5.0 builds on the proven track record of previous versions of our Relationship Modeling Engine" explains Yoram Nelken, founder and chief technology officer of Banter.
"With a broader and deeper scope, RME version 5.0 is able to deliver a higher level of accuracy in analyzing everyday language for the ever-expanding volume of human communication and requires minimal administrative effort and IT maintenance."
Using unique semantic modeling techniques, the system can automatically apply knowledge learned from one channel (such as e-mail) into other channels (such as self-service or chat). Additionally, real-time feedback improves classification accuracy and automation ability to an even greater degree, and significantly reduces maintenance costs, allowing ever-improving accuracy.
Based in San Francisco, Banter has offices in New York, Boston and Israel and is backed by Mayfield Fund, Lucent Venture Partners Inc., and Israel Seed Partners among others.
Banter's customers range from members of the Fortune 500 to emerging companies and include such global entities as ABN AMRO America, VeriSign, and Wells Fargo Bank
www.banter.com
Delicious
Digg
Reddit
Magnoliacom
Newsvine
Furl
Google
Yahoo
Technorati
Comments
Post new comment