Elsevier Text Mining

Elsevier Text Mining enables the retrieval of highly specified information from unstructured content, providing more meaningful answers to complex research questions.

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Visit the Hive to explore how biotech and pharma start-ups are using Elsevier R&D solutions to solve for early-stage drug discovery and development. Watch videos, read real-world case studies and learn how early innovation starts with rapid results and early access to research that is crucial to reducing the likelihood of costly late stage failures and mitigating risk.

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Rapid Access to Relevant Answers

Discover how Elsevier Text Mining helps life sciences researchers discover insights, extracting detailed information from unstructured content.

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Rapid Access to Relevant Answers

Find Relevant Answers

The information demands of life science and pharmaceutical R&D teams are considerable and complex—they need to answer very specific questions about disease mechanisms, compound bioactivities, drug efficacies and competitor product performances. Retrieving such specific information from the vast amount of in-house and third-party content demands considerable expertise in search construction.

Text Mining Based on Life Science Expertise

Elsevier Text Mining takes the concept of mining databases to a level beyond traditional search engines. Backed by expertise in classifying life science data and natural language processing, this tool helps even novice users pinpoint precise data within unstructured content, including full-text literature or specified sections of literature (e.g., within materials and methods sections). The data output can also be customized—databases of extracted facts, full-text literature and/or citations are all possible.

Discover more in our case study: Mining Text to Deliver Answers on Demand (PDF, 646kb)

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