“I’ve got invoices, tables, forms and a mix. Do I use GLYNT or NLP? Is GLYNT NLP?”
We get this question a lot. The short answer is “yes.” Natural Language Processing (NLP) is concerned with how to program computers to process and analyze large amounts of natural language data. One approach uses machine learning (ML) techniques. GLYNT is an ML system that is focused on delivering highly accurate data in the the form of label/data value from complex documents such as invoices, tables, forms and so on. So in some sense, GLYNT is dealing with non-natural language 🙂
Let’s take a look at some ML results for document classification and extracting unstructured data from documents:
- Dropbox: 87% accuracy after training on more than 300,000 invoices
- Cloudscan: 84% accuracy after training on more than 326,000 invoices
- Jatana.ai, #2: 75% accuracy after training on 153 documents using one ML method
- Jatana.ai, #3: 85% accuracy after training on more than 14,000 documents using another ML method
- GLYNT: 98% accuracy after training on 2 – 7 invoices
Other companies, such as butter.ai, acquired by Box, have used similar methods to accomplish the these tasks.
For example, healthcare records contains both precise numeric data, such as lab results, and wordy paragraphs of text, the clinical notes made by the doctor. Some ML techniques, often used by NLP specialists will be used to create summaries and extract context from the text. GLYNT can be used in the other parts of the record, delivering the precise numerical data with rigor. The mix is the winner.
So, when we’re asked the question “Is GLYNT NLP?” it won’t surprise you to hear that our answer is “Yes. And tell me about the problem you want to solve.”