Building the future of text analytics
 

SciBite CSO and Founder Lee Harland was recently featured in KM World Magazine, where he discusses how deep learning (DL) has emerged as a compelling approach to address many common use cases, including Named Entity Recognition (NER), which represents a step-change in machine understanding of the human-written text.

Text analytics plays a major role in unlocking insight within internal repositories in the Life Sciences industry, but many forward-thinking CTOs and CIOs are facing three major challenges:

• Change management: How to utilize new algorithms without breaking existing workflows leading to expensive remediation
• Model training: Published models will only work in specific environments. Additional infrastructure is required to tailor DL models to specific internal business questions. Costs to train and deploy these models are estimated to be $10,000+ for each iteration
• Annotation consistency: As these models evolve over time, how can one ensure consistent behaviour (A company’s products are correctly identified each time)

SciBite’s award-winning text analytics platform is used by the world’s largest science-based businesses to address these issues. SciBite’s ontology-led solutions understand the complexity and variability of scientific content and that ontologies are the de facto standard to encode semantics in an understandable form for both humans and machines.

Check out the full article on the KM World website to learn more.

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