Inside Big Data: Text Analytics without Tradeoffs
The pace at which the world creates data will never be this slow again. And much of this new data we’re creating is unstructured, textual data. Emails. Word documents. News articles. Blogs. Reviews. Research reports…
Understanding what’s in this text – and what isn’t, and what matters – is critical to an organization’s ability to understand the environments in which it operates. Its competitors. Its customers. Its weaknesses and its opportunities.
In recent years, many new text analysis solutions have come to market. But these solutions have forced customers to choose. Speed or scale. Accuracy or ease of use. Customizability or interoperability.
It shouldn’t be that way. And it isn’t any longer.
Finch for Text is an entity extraction and disambiguation solution that employs natural language processing, sophisticated statistical models and other heuristics to isolate and extract eight distinct entity types from unstructured text.
Lots of it. Very quickly. And very accurately.
Finch for Text leverages a proprietary in-memory computing platform, combined with sophisticated algorithms, to deliver true entity disambiguation – not just resolution or type-matching.
It’s a completely new way of interacting with information. At a time when it’s needed most.
See full article here.