Parsing Natural Language is one of the most advanced fields in Computer Science, as it is highly unstructured. Still, it is part of so many use cases and tools. Customers send questions via mail. Doctors explain diagnoses in findings. Products are described on websites. Named Entity Recognition (NER) is the field of Machine Learning in which you can extract semantic structures from such texts. It is a core technology, needed in various scenarios of every company.
As with any Supervised Learning scenario, you need plenty of labeled training data. Your model must be capable of making token-level classifications (for now you can assume that every word is exactly one token), which can become quite difficult. With kern, building such NER applications becomes that much easier. To address this issue, our system helps you to both scale your labeling to build large training datasets, and also improves existing labeled data via extensive data management capabilities.
If you want to give it a try - kern comes with a free version. And we’re more than happy to help you with any questions you might have.