Deep learning models can be trained to understand language and the context of how words are used in sentences. Instead of providing an algorithm with rules, they can be taught using examples which they can subsequently generalise and learn from. It is this emphasis on pattern recognition that enables them to be applied to situations where pre-defined rules don’t exist. However, the accuracy of such models is highly dependent on the quality of the training data used to build them.
SciBiteAI combines the flexibility of deep learning pattern recognition with the reliability of SciBite’s semantic technologies. The use cases below highlight the power of SciBiteAI, which provides a framework to incorporate different Machine Learning approaches, ensuring that it can be applied to a wide range of problems.
To learn more, download the full use case.
SciBite announces the release of SciBiteAI Relationship Extraction models, which provide the enhanced ability to identify complex relationships within text to further unlock insights from Life Sciences data.Read
SciBite announces the launch of SciBiteAI, a state-of-the-art Artificial Intelligence software platform for leveraging machine learning models alongside semantic technologies to unlock insights into Life Sciences data.Read
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