The Evolution of Text Mining – Trends We’re Seeing Across R&D Organizations appears on the Copright Clearinghouse website and was written by Mike Larrobino.
“It’s no secret that information available in the digital ecosystem is rapidly expanding. Three million papers are published in scholarly journals annually, and that’s just one high value content type for R&D–intensive organizations that may also need quick and easy synthesis of patent, clinical, and other types of content.
In the past decade, R&D-intensive companies increasingly rely on text mining to glean important insights from vast amounts of published material. When we think about text mining, we’re really thinking about processing larger amounts of data. After all, there are natural limits to the qualitative analysis an individual can do as a researcher in a specific business function when dealing with an abundance of information.
In past years, we have observed more of a project-based approach to text and data mining. There would be a specific ‘ask’ sponsored by some area of the business, and text mining would be applied to deliver a particular result, answer, or response through machine analysis. Although a text mining application or a tool may have been used, it was ad hoc for the particular project, and its use often waned after project completion.
As time has gone on and much more data is being produced, there’s a broadening set of applications for text and data mining – including those where text mining is ‘baked in’ to an end user information experience, and where text mining is applied as part of an ongoing data processing pipeline. There are a lot of different factors that can be considered as you look at the different options for how it might be implemented in an organization.
Based on the trends we’ve seen among clients, we’d recommend considering the following:..“
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