Text mining is the ultimate solution for large-scale research. Part mega search engine, part smart data, it takes unstructured text, breaks it down into smaller chunks, and helps researchers interpret meaning.
Some are still not sure that text mining is necessary. Some still think that web searching and online information sources are enough. But a new e-book from the Copyright Clearance Center shows that text mining goes well beyond document search to find facts and assertions in the literature and derive new value.
For example, keyword search or manual reading could never examine the more than 25 million articles indexed in MEDLINE to identify relationships between classes of concepts like genes and diseases, irrespective of phrasing. Only text mining can handle that level of volume and complexity.
It can be applied at all stages of the R&D process, including:
- Early phase research
- Clinical trial development
- Drug safety monitoring
- Competitive intelligence.
The challenge, however, comes with content access preparation, and licensing.
Check out the e-book from the Copyright Clearance Center that covers four misconceptions about text mining, as well as the new "reality" of each of these situations.
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