Semantic solution to search relevancy

RiverGlass has launched Release 3.0 of its Semantic Text Analyzer (STAn), promising a solution to the perennial problem of wading through pages of irrelevant search results.

The company claims search solutions based on traditional statistical natural language processing (NLP) approaches have fundamental limitations. These limitations come down to an inability to understand the meaning of what is being said or written -- a particularly vexing problem when it comes to differentiating the multiple meanings words commonly have.

Doug Marquis, Vice-President of Product Development at RiverGlass observes: "The addition of ontological semantics takes our text analysis capabilities to a totally new level, allowing us to bring to market the first true semantic, meaning-based approach to search and text analytics. There is no other vendor offering these capabilities today."

STAn 3.0 is the result of a multi-year R&D effort of leading computational linguists and contains a foundation of language-independent, universal concepts to which language- and application-dependent dictionaries can be tightly coupled in order to address specific application areas of interest within a business or market such as legal/eDiscovery, pharmaceutical, financial services or health care.

"The introduction of the semantic ontology developed by RiverGlass is an exciting opportunity for customers and partners to build applications that bring a focused meaning not previously possible to the massive volume and variety of data that business users face every day," stated Kirk Dauksavage, RiverGlass' CEO.

"It is all about finding and understanding what content is actually of interest to someone -- and we are now positioned to do that better than anyone else given our semantic technology," concluded Dauksavage.


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