Cleansing your data in a scalable manner is one of the larger problems of working with unstructured texts. For instance, if you collect data from various logistics platforms that each enable their users to enter their payload data in form of a comment, it can become extremely cumbersome to parse the data correctly.
Named Entity Recognition models can be applied to collect the data from an unclean text, but building the respective large-scale dataset is quite difficult. With kern, it becomes much easier. By applying several information sources such as regular expressions or pre-trained ML extractors and synthesizing them using Weak Supervision, we can help you scale the labeling quickly. With further data management techniques, you can also identify potential errors in your dataset and improve on them, so that your solution becomes more and more precise.
If you’re interested in trying out kern, you can register for our free version. If you have any questions about the usage or run into problems, we’re there to guide you along the way.